Data compression modeling

  • How to do model compression?

    4 Popular Model Compression Techniques Explained

    1. The pruning technique
    2. The quantization technique
    3. The knowledge distillation technique
    4. The low-rank factorization technique

  • Text compression techniques

    Shannon formulated the theory of data compression.
    Shannon established that there is a fundamental limit to lossless data compression.
    This limit, called the entropy rate, is denoted by H.
    The exact value of H depends on the information source --- more specifically, the statistical nature of the source..

  • What is a compression model?

    As the name suggests, model compression helps reduce the size of the neural network without compromising too much on accuracy.
    The resulting models are both memory and energy efficient.
    Many model compression techniques can be used to reduce the model size..

  • What is modeling in data compression?

    Modelling of the source is intended to extract information from the source in order to guide the coder in the selection of proper codes.
    The models may either be given a priori (static) or may be constructed on-the-fly (dy - namic, in adaptive compression) throughout the compression process..

  • What is Modelling in data compression?

    Characterization of redundancy involves some form of modeling.
    Hence, this step in the compression process is also known as modeling.
    For historical reasons another name applied to this process is decorrelation.
    After the redundancy removal process the information needs to be encoded into a binary representation..

Data compression involves the development of a compact representation of information. Most representations of information contain large amounts of redundancy.
One way of classifying compression schemes is by the model used to characterize the redundancy. However, more popularly, compression schemes are divided into 

Is there a solution to model compression in deep learning?

Do take into consideration that in Deep Learning which extends to model compression, there is no hard and fast solution to any problem. the fact of the matter is that it is hard to tell how much savings we would get, the best we can do is try it out ourselves and analyze whether there is an improvement in model size with little loss in accuracy.


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