Fine-Tuning Stable Diffusion to Create a Virtual Fashion Designer for Customers

January 29, 2024
By
Manthan Abhay Deshpande
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Table of Contents

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Introduction

The world of online shopping is changing quickly and consumers are expecting more individualized and engaging experiences. In this blog, we set out to build a virtual changing room using artificial intelligence (AI). Our goal is to provide users with the ability to upload their own photos and see life-like models of themselves in different outfits, providing a fresh and entertaining way for people to experiment with different looks.

Problem Statement

One of the most frequent problems customers in online retail encounter is trying to picture how a dress would appear on them. Our goal is to overcome this difficulty by creating a virtual changing room where users can upload pictures of themselves and see life-like simulations of themselves wearing various outfits. This not only makes online shopping more enjoyable; it also adds some creativity and fun to the process.

What Is Stable Diffusion?

A generative artificial intelligence (AI) model called Stable Diffusion can use text and image prompts to produce photorealistic images, videos, and animations. This deep learning model has the ability to translate written descriptions into intricate visuals.

Stable diffusion models use text or visual cues to produce graphics, videos, and animations. By using a latent diffusion model (LDM) that has been painstakingly trained on a variety of real-world imaging datasets, these models are able to provide outputs that are incredibly detailed and life-like.

Because the generated pictures' artistic style and content may be altered by the user, Stable Diffusion Models are incredibly flexible tools for developers and designers. These models are a part of a broader trend of artificial intelligence (AI)-driven creative tools that are revolutionizing digital art and content creation.

  • Realistic images can be produced using generative AI technology.
  • Makes use of a Latent Diffusion Model that was developed on actual photos.
  • Gives the user discretion over content and style.

How Can I Access Stable Diffusion Models?

Several websites that provide AI models offer access to downloads of Stable Diffusion Models. Two well-known repositories where users can access a variety of Stable Diffusion Models, each with special traits and abilities, are Civitai and Hugging Face.

User manuals and paperwork are frequently included with these devices to help with setup and operation. Furthermore, some models include built-in safety filters to check the creation of explicit content, but it's vital to remember that these filters are not infallible.

  • Available for download on websites like Civitai and Hugging Face.
  • User manuals and documentation are normally supplied.
  • Certain models come with safety filters.

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Why Is Stable Diffusion Important?

Because Stable Diffusion is readily available and simple to use, it is significant. Graphics cards suitable for consumers can run it. For the first time, anyone can download the model and create their own images. Important hyperparameters that you can adjust include the amount of noise applied and the number of denoising steps. 

Stable Diffusion is easy to use, and it doesn't require any extra knowledge to generate images. Because of its vibrant community, Stable Diffusion has a wealth of tutorials and documentations. The program can be used, altered, and redistributed under the terms of the Creative ML OpenRAIL-M license.

What Architecture Does Stable Diffusion Use?

Text conditioning, a noise predictor, forward and reverse diffusion, and a variational encoder are the primary architectural elements of stable diffusion.

Autoencoder with Variation

There is a separate encoder and decoder for each variational autoencoder. The 512x512 pixel image is compressed by the encoder into a more manageable 64x64 model in latent space. The decoder converts the model back into a full-size 512x512 pixel image from latent space.

Forward Dispersion

Gaussian noise is gradually added by forward diffusion to an image until only random noise is present. From the final noisy image, it is impossible to determine what the image was. Every image goes through this process while it is being trained. Other than image-to-image conversion, forward diffusion is not used any more.

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Reverse Diffusion

This procedure basically undoes the forward diffusion iteratively using a parameterized approach. A dog and a cat are two examples of the two photos you may use to train the model. If you did, the opposite process would go in the direction of a dog or a cat, with no intermediate stops. In real life, model training creates unique visuals by using prompts on billions of photographs.

U-Net Noise Predictor

The secret to denoising photos is a noise predictor. A U-Net model is used by Stable Diffusion to accomplish this. Convolutional neural networks, or U-Net models, were first created for image segmentation in the biomedical field. Specifically, the Residual Neural Network (ResNet) model created for computer vision is used in Stable Diffusion.

Use Case of Stable Diffusion

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Stable Diffusion is unlike many other diffusion models. Diffusion models encode images in theory using Gaussian noise. Subsequently, they replicate the image using a reverse diffusion method and a noise predictor. Stable Diffusion is distinct from other diffusion models not just in its technical aspects but also in that it does not utilize the image's pixel space. Rather, it makes use of a latent space with decreased definition.  

This is due to the fact that there are 786,432 potential values for a color image with 512 x 512 resolution. In contrast, Stable Diffusion makes use of a compressed image with 16,384 values, which is 48 times smaller. Processing requirements are greatly decreased as a result. 

At the core of our solution lies the Stable Diffusion AI model, designed for image generation and manipulation. Fine-tuned specifically for clothing modifications, this model acts as the creative engine behind our virtual dressing room, delivering realistic and visually appealing results.

Dataset: Images of a Customer, Product Images of a Dress

Our dataset comprises a diverse collection of customer images and product images of different dresses. This dataset serves as the training ground for our AI model, allowing it to understand various clothing styles and generate compelling simulations.

Why Advanced GPUs Are Necessary

Running Stable Diffusion models requires a powerful dedicated GPU because of a number of computationally intensive requirements related to the model's architecture and training procedure.

In the figure below, a typical GPU architecture is displayed. However, developers can usually obtain the same capabilities through a cloud GPU platform rather than purchasing sophisticated GPUs. You can leverage the GPU stack's capabilities, such as GPU clusters, faster bandwidth, and memory efficiency, with the best cloud GPU architectures.

a realistic photograph of a gpu on fire <lora:add_detail:0.9>

Why advanced GPUs are necessary:

Computational Intensity: Complex operations such as forward and reverse diffusion, noise prediction, and image generation are involved in Stable Diffusion models. Although these operations require a significant amount of processing power, the complex calculations involved can be effectively handled by a powerful GPU.

Model Dimensions and Architecture: Latent Diffusion models usually function in a space with a large number of dimensions. To efficiently handle this large latent space, computations of this nature call for a powerful GPU with parallel processing capabilities. Complex operations are carried out by the VAE component, which encodes and decodes images. The computations are accelerated by a dedicated GPU, especially when working with high-resolution images.

High-Resolution Image Generation: Images with 512x512 pixels or higher in resolution are frequently produced by Stable Diffusion models. This resolution of image processing requires a significant amount of memory and computational resources.

E2E Networks: A Cloud-Based Dedicated GPU Platform

Leading Indian hyperscaler E2E Networks specializes in cutting-edge Cloud GPU infrastructure. We offer solutions for accelerated cloud computing, such as the AI Supercomputer HGX 8xH100 GPUs and state-of-the-art Cloud GPUs like A100/H100. We provide a selection of cutting-edge cloud GPUs at incredibly low prices. Go here to learn more about the products that E2E Networks offer. The optimal GPU for using the Stable Diffusion model will mostly depend on your needs and price range. I made use of an A100–80 GB GPU-dedicated compute.

To proceed with E2E Networks, add your SSH key by going to Settings.

Then create a node by going to Compute.

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Launch Visual Studio Code and download the Remote Explorer and Remote SSH extensions. Launch a fresh terminal. To gain access to your local system, just enter the code below:

ssh root@<your public ip address>

SSH will be used to log you in remotely on your local computer. Let's begin putting the code into practice now.

Step-by-Step Guide to Fine-Tuning Stable Diffusion to Create a Virtual Fashion Designer for Customers

Part 1: Launching Node and Downloading Model

Our journey commences with the setup of the computing environment. We launch a node on E2E Cloud and download the Stable Diffusion model.


# Install necessary libraries
!pip install -q matplotlib
!pip install -q numpy
!pip install -q pandas
!pip install -q scikit-learn
!pip install opencv-python -q
!pip install pyarrow pillow -q
!pip install keras-cv==0.6.0 -q
!pip install -U tensorflow -q
!pip install keras-core -q


# Import libraries
import os
import warnings
warnings.filterwarnings("ignore")
import keras_cv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.experimental.numpy as tnp
import cv2
from PIL import Image
from textwrap import wrap
from keras_cv.models.stable_diffusion.clip_tokenizer import SimpleTokenizer
from keras_cv.models.stable_diffusion.diffusion_model import DiffusionModel
from keras_cv.models.stable_diffusion.image_encoder import ImageEncoder
from keras_cv.models.stable_diffusion.noise_scheduler import NoiseScheduler
from keras_cv.models.stable_diffusion.text_encoder import TextEncoder
from tensorflow import keras

The installation of required libraries ensures a well-equipped environment for seamless execution. We then import essential libraries and set paths for our image and text data.

Part 2: Gathering Fine-Tuning Data

Next, we load images and text descriptions, creating a structured DataFrame. The data is filtered based on specific keywords related to clothing styles.

You can download the images dataset from here and the text descriptions as well from here.

During the training process, we used both detailed text and visual elements in the image which are in the datasets.


# Specify the paths to your image and text description files
images_dir = "/path/to/your/images/directory"
text_descriptions_file = "/path/to/your/text/descriptions/file.txt"

# Load images from the directory
image_files = os.listdir(images_dir)
image_paths = [os.path.join(images_dir, file) for file in image_files]

# Load text descriptions from the file
with open(text_descriptions_file, 'r') as file:
    text_descriptions = file.readlines()

# Create a DataFrame with image paths and text descriptions
data = {'image': image_paths, 'text': text_descriptions}
df = pd.DataFrame(data)

# Specify the keywords for fine-tuning
keywords = ["latex short black dress", "pantyhose", "white oversized coat"]

# Filter the dataset based on keywords
filtered_df = df[df['text'].str.contains('|'.join(keywords), case=False)]

This step establishes the foundation for training our model by organizing the data and filtering out irrelevant entries using predefined keywords.

Part 3: Fine-Tuning Stable Diffusion

We prepare the model for fine-tuning by setting up components such as the image encoder, diffusion model, and trainer. We define hyperparameters and initiate the training process.

You can download the model from here.


# Display a sample of the filtered dataset
filtered_df.sample(5)

# Define constants for fine-tuning
RESOLUTION = 256
MAX_PROMPT_LENGTH = 77
PADDING_TOKEN = 49400

# Load the pretrained model from the .safetensors file
pretrained_model_path = "/path/to/your/pretrained_model.safetensors"
pretrained_model = tf.saved_model.load(pretrained_model_path)

# Define the tokenizer and text encoder for fine-tuning
tokenizer = SimpleTokenizer()
text_encoder = TextEncoder(MAX_PROMPT_LENGTH)

Fine-tuning the model involves configuring essential components and defining parameters for effective learning. We also consider mixed-precision training for enhanced efficiency.

Part 4: Showcasing Prompting

To effectively train our model, we create a function to process text for fine-tuning and tokenize the text data using a tokenizer.


# Define a function to process text for fine-tuning
def process_text_for_fine_tuning(text):
    tokens = tokenizer.encode(text)
    tokens = tokens + [PADDING_TOKEN] * (MAX_PROMPT_LENGTH - len(tokens))
    return np.array(tokens)

# Tokenize the text for fine-tuning
tokenized_texts = np.array([process_text_for_fine_tuning(text) for text in filtered_df['text']])

Text processing is a crucial step, ensuring that our AI model comprehends input prompts effectively. Tokenization converts textual data into a format suitable for training.


# Define the image augmentation pipeline
augmenter = keras.Sequential(
    layers=[
        keras_cv.layers.CenterCrop(RESOLUTION, RESOLUTION),
        keras_cv.layers.RandomFlip(),
        tf.keras.layers.Rescaling(scale=1.0 / 127.5, offset=-1),
    ]
)

Part 5: Training the Model

Demonstrate the application of the trained model for clothing modification. We utilize a dedicated Trainer class and initiate the training process.


# Define the Trainer class for fine-tuning
class Trainer(tf.keras.Model):
    def init(self, diffusion_model, vae, noise_scheduler, use_mixed_precision=False, max_grad_norm=1.0, **kwargs):
        super(Trainer, self).init(**kwargs)
        self.diffusion_model = diffusion_model
        self.vae = vae
        self.noise_scheduler = noise_scheduler
        self.max_grad_norm = max_grad_norm
        self.use_mixed_precision = use_mixed_precision
        self.vae.trainable = False

    def train_step(self, inputs):
        images = inputs["images"]
        encoded_text = inputs["encoded_text"]
        batch_size = tf.shape(images)[0]
with tf.GradientTape() as tape:
            latents = self.sample_from_encoder_outputs(self.vae(images, training=False))
            latents = latents * 0.18215
            noise = tf.random.normal(tf.shape(latents))
            timesteps = tnp.random.randint(0, self.noise_scheduler.train_timesteps, (batch_size,))
            noisy_latents = self.noise_scheduler.add_noise(tf.cast(latents, noise.dtype), noise, timesteps)
            target = noise
            timestep_embedding = tf.map_fn(lambda t: self.get_timestep_embedding(t), timesteps, dtype=tf.float32)
            timestep_embedding = tf.squeeze(timestep_embedding, 1)
            model_pred = self.diffusion_model([noisy_latents, timestep_embedding, encoded_text], training=True)
            loss = self.compiled_loss(target, model_pred)
            if self.use_mixed_precision:
                loss = self.optimizer.get_scaled_loss(loss)

        trainable_vars = self.diffusion_model.trainable_variables
        gradients = tape.gradient(loss, trainable_vars)
        if self.use_mixed_precision:
            gradients = self.optimizer.get_unscaled_gradients(gradients)
        gradients = [tf.clip_by_norm(g, self.max_grad_norm) for g in gradients]
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))

        return {m.name: m.result() for m in self.metrics}

    def get_timestep_embedding(self, timestep, dim=320, max_period=10000):
        half = dim // 2
        log_max_period = tf.math.log(tf.cast(max_period, tf.float32))
        freqs = tf.math.exp(-log_max_period * tf.range(0, half, dtype=tf.float32) / half)
        args = tf.convert_to_tensor([timestep], dtype=tf.float32) * freqs
        embedding = tf.concat([tf.math.cos(args), tf.math.sin(args)], 0)
        embedding = tf.reshape(embedding, [1, -1])
        return embedding

    def sample_from_encoder_outputs(self, outputs):
        mean, logvar = tf.split(outputs, 2, axis=-1)
        logvar = tf.clip_by_value(logvar, -30.0, 20.0)
        std = tf.exp(0.5 * logvar)
        sample = tf.random.normal(tf.shape(mean), dtype=mean.dtype)
        return mean + std * sample

    def save_weights(self, filepath, overwrite=True, save_format=None, options=None):
        self.diffusion_model.save_weights(filepath=filepath, overwrite=overwrite, save_format=save_format, options=options)

Training the model involves specifying hyperparameters, defining a checkpoint for saving weights, and executing the training process. This step fine-tunes the model for accurate clothing modifications.


# Enable mixed-precision training if the underlying GPU has tensor cores.
USE_MP = True
if USE_MP:
    keras.mixed_precision.set_global_policy("mixed_float16")

image_encoder = ImageEncoder()
diffusion_ft_trainer = Trainer(
    diffusion_model=DiffusionModel(RESOLUTION, RESOLUTION, MAX_PROMPT_LENGTH),
    vae=tf.keras.Model(image_encoder.input, image_encoder.layers[-2].output),
    noise_scheduler=NoiseScheduler(),
    use_mixed_precision=USE_MP,
)

# Hyperparameters
lr = 1e-5
beta_1, beta_2 = 0.9, 0.999
weight_decay = 1e-2
epsilon = 1e-08

# Optimizer
optimizer = tf.keras.optimizers.experimental.AdamW(
    learning_rate=lr,
    weight_decay=weight_decay,
    beta_1=beta_1,
    beta_2=beta_2,
    epsilon=epsilon,
)
diffusion_ft_trainer.compile(optimizer=optimizer, loss="mse")

Now, let’s train for 100 epochs.


# Training
epochs = 100
ckpt_path = "finetuned_stable_diffusion.h5"
ckpt_callback = tf.keras.callbacks.ModelCheckpoint(
    ckpt_path,
    save_weights_only=True,
    monitor="loss",
    mode="min",
)
diffusion_ft_trainer.fit(training_dataset, epochs=epochs, callbacks=[ckpt_callback])

Results

Now, let's showcase the results of our virtual dressing room by modifying the clothing in an example image.


# Text-to-Image Generation with Clothing Modification
def modify_clothing(image_path, prompt):
    input_image = tf.io.read_file(image_path)
    input_image = tf.io.decode_png(input_image, 3)
    input_image = tf.image.resize(input_image, (RESOLUTION, RESOLUTION))
    tokenized_prompt = process_text_for_fine_tuning(prompt)
    input_image = tf.expand_dims(input_image, axis=0)
    tokenized_prompt = tf.expand_dims(tokenized_prompt, axis=0)
    augmented_image, encoded_prompt = apply_augmentation(input_image, tokenized_prompt)
    _, _, encoded_text_batch = run_text_encoder(augmented_image, encoded_prompt)
    modified_image = diffusion_ft_trainer.diffusion_model.predict([augmented_image, encoded_text_batch])
    return modified_image[0]

# Example Usage
input_image_path = '/path/to/your/input/image.png'
prompt_for_clothing_modification = "Change to suit"

modified_image_output = modify_clothing(input_image_path, prompt_for_clothing_modification)

The example demonstrates the transformation of an input image based on the provided prompt for clothing modification. The side-by-side comparison of the input and modified images allows users to witness the AI-driven changes in attire.

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Conclusion

In conclusion, the Stable Diffusion model's fine-tuning for e-commerce image generation was greatly improved by the integration of E2E Networks' A100–80 GB GPU dedicated compute. The computational power of the A100 GPU effectively handled complex model operations, leading to faster training and the seamless process of image generation, noise prediction, forward and reverse diffusion.

The versatility of the A100 allowed for quick experimentation and effective model customization through fine-tuning on unique datasets. The A100 GPU guaranteed responsiveness for real-time image generation, cutting down on training times and improving user experience. The cloud-based infrastructure from E2E Networks offered a customizable setting that did away with hardware limitations and made dedicated GPU resources available.

In summary, the synergistic environment that was created by the partnership between E2E Networks’ A100 GPU and Stable Diffusion model fine-tuning was marked by accessibility, computational efficiency, and accelerated model training, making the process of creating visual content for e-commerce both efficient and pleasurable.

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A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

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  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

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  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

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The Main Objective of the 3D Object Reconstruction

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State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

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  • Input

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The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website. 

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

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For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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