CNN is a very marketable and profitable technology in recent times. Neural networks are a part of almost all the big breakthroughs and inventions in the business forum that are connected with technology. With neural networks, working with machine learning and deep learning technology has become very easy. Even the more complex and important image data can be retrieved with the help of neural networks.
It can sometimes be hard to deal with Conventional Neural Networks but it gives the best results. It is the most accurate form of retrieving image data. CNN candidate any image or object and you can easily retrieve the data even from complex images. Neural networks have become an inalienable part of various organizations and have become a go-to technology for image data.
What is CNN?
Conventional Neural Networks are the most used form of technology when dealing with image inputs and data. An image is taken as an input and then classified into various other output categories when CNN deals with a particular image or object. A conventional neural network cannot exist without layers.
A CNN contains multiple layers and each layer is followed by another. To get placed in an output class, any image or object has to pass through each of these layers. With the help of CNN, extraction of multiple features from an image takes place which could not be identified when using simple neural networks.
What is Layer Wise Data Free compression?
Data-free compression starts with a fully trained and equipped network and a similar compressed network is created with the same structure. The architecture that is created for data-free compression is called the 'Student' network and the network from where the data is retrieved is called the 'Parent' network.
The layer optimization for the neural networks can be done with the following measures:
- Fusion and Equalization: Two major compressed individual layers are involved in this method. The first issue is connected to the BatchNorm layer. A BatchNorm layer is usually used in CNN that comprises the various parameters.
- Layer-wise data generation: This layer uses layer-wise optimization for generating data. This method is generally used for data pruning and quantization. This provides data-free compression.
- Data-free quantization: This method is mainly used for the quantization of neural networks. This methodology quantized both activation ranges and weights.
- Data-free Pruning: Pruning is used in generating data for pruning and managing weights. It is done by duplicating networks that are already trained. Each layer is pruned and duplicated as a Student network.
These data-free layer-wise methodologies are used to deal with compression neural networks to deal with image data and inputs. Certain experiments have also been performed to test these methods and all of them have turned out to be successful for CNN functioning.
Conventional Neural Networks have become an integral part of every company and organization that deal with image data incorporating CNN for extracting every detailed information about the image. It also helps to classify the input image into the perfect output that is best suited for the input. You can also read about Implementing CNN in PyTorch with Custom Dataset and Transfer Learning for getting a better understanding of Neural Networks in AI and explore the same.