Centered Kernel Alignment (CKA) - PyTorch Implementation

A PyTorch implementation of Centered Kernel Alignment (CKA) with GPU support.

This code was used for the CKA analysis in our CVPR 2023 paper, "On the Stability-Plasticity Dilemma of Class-Incremental Learning".

Usage

model1 = ... # Some model, casted to GPU
model2 = ... # Another model, casted to GPU
dataloader = ... # Your dataloader

calculator = CKACalculator(model1, model2, dataloader)
cka_matrix = calculator.calculate_cka_matrix()

Rather than caching intermediate feature representations, this code computes CKA on-the-fly (simultaneously with the model forward pass) by using the mini-batch CKA, as described in the paper by Nguyen et. al. By leveraging GPU superiority, this implementation runs much faster than any Numpy implementation.

Setup

I haven't added a requirements.txt since the exact version of each package is not that important :man_shrugging:

Required packages to use the class/functions:

• python3.7+
• torch (any relatively recent version should be O.K.)
• torchvision
• tqdm
• torchmetrics

To run the example.ipynb:

• jupyter
• matplotlib
• numpy

Example notebook

Try out the example notebook in example.ipynb.

Other

• If you found this repo helpful, please give it a :star:
• If you find any bugs/improvements, feel free to create a new issue.
• This code is mostly tested on ResNets

TODO (when I feel like it)

• Ditch hooks; change to torch.fx implementation