Compression

Compression is a technique used in machine learning to reduce the storage requirements of trained models, without significantly impacting their accuracy or performance. It involves using algorithms to compress the weights and activations of a model, which can greatly reduce the amount of memory required to store the model.

There are different types of compression algorithms used in machine learning, such as:

Weight sharing:

This technique involves sharing the same weight value between multiple connections in the neural network, which can greatly reduce the number of weights required to store the model.

Huffman coding:

This technique involves encoding weights using a variable-length code, where frequently occurring weights are represented by shorter codes, and less frequent weights are represented by longer codes.

Singular value decomposition (SVD):

This technique involves factorizing the weight matrix of a model into smaller matrices, which can reduce the number of weights required to store the model.

Pruning:

This technique involves removing unnecessary connections or neurons from a model, which can reduce its size and improve its speed, as mentioned in the previous answer.

Compression techniques can be applied to a model after it has been trained and can be used in conjunction with other optimization techniques, such as quantization and pruning, to further reduce the size and complexity of a model. Compression can also be applied iteratively, where a model is compressed and then retrained, to achieve even greater reductions in size and complexity.

Overall, compression is a useful technique for reducing the storage requirements of machine learning models, particularly in scenarios where memory is limited, such as on embedded devices or in cloud-based applications with high storage costs.