memorywrap 1.0.5

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,mlp_activation=torch.nn.ReLU())

or, for the baseline that uses only the memory to output the prediction:

memorywrap = BaselineMemory(encoder_dim,output_dim,mlp_activation=torch.nn.ReLU())

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;
• mlp_activation s the activation function that must be used in the hidden layer of multi-layer perceptron.

Forward call

Add the forward call to your forward function.

output_memorywrap = memorywrap(input_encoding,memory_encoding,return_weights=False)

where input_encoding and memory_encoding are the outputs of the the encoder of rispectively the current input and the memory set.
The last argument of the Memory Wrap's call function is a boolean flag controlling the number of outputs returned. If the flag is True, then the layer returns both the output and the sparse attention weight associated to each memory sample; if the flag is False, then the layer return only the output.

Additional information

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

• Paper
• GitHub repo
• Jupyter notebook