memorywrap 1.0.3

Memory Wrap: an extension for image classification models

Description

Memory Wrap is an extension to image classification models that improves both data-efficiency and model interpretability, adopting a sparse content-attention mechanism between the input and some memories of past training samples.

Installation

This is a PyTorch implementation of Memory Wrap. To install Memory Wrap run the following command:

pip install memorywrap

The library contains two main classes:

• MemoryWrapLayer: it is the Memory Wrap variant described in the paper that uses both the input encoding and the memory encoding to compute the output;
• BaselineMemory: it is the baseline that uses only the memory encoding to compute the output.

Usage

Instantiate the layer

memorywrap = MemoryWrapLayer(encoder_dim,output_dim,return_weights=False)

or

memorywrap = BaselineMemory(encoder_dim,output_dim)

where:

• encoder_dim is the output dimension of the last layer of the encoder
• output_dim is the desired output dimensione. In the case of the paper output_dim is equal to the number of classes;
• return_weights is a flag telling to the layer if it has to return the sparse content weights.

Forward call

Add the forward call to your forward function.

output_memorywrap = memorywrap(input_encoding,memory_encoding)

where input_encoding and memory_encoding are the outputs of the the encoder of rispectively the current input and the memory set.
If you have set the flag return_weights to True, then output_memorywrap is a Tuple where the first element is the output and the second one are the content weights associated to each element in the memory_encoding.

Additional information

Here you can find link to additional source of information about Memory Wrap:

• Paper
• GitHub repo
• Jupyter notebook