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Deep Unlearning via Randomized Conditionally Independent Hessians (CVPR 2022)

[Paper] [Slide]

Ronak Mehta*, Sourav Pal*, Vikas Singh, Sathya N. Ravi

(* Joint First authors)

Abstract

Recent legislation has led to interest in machine unlearning, i.e., removing specific training samples from a predictive model as if they never existed in the training dataset. Unlearning may also be required due to corrupted/adversarial data or simply a user’s updated privacy requirement. For models which require no training (k-NN), simply deleting the closest original sample can be effective. But this idea is inapplicable to models which learn richer representations. Recent ideas leveraging optimization-based updates scale poorly with the model dimension d, due to inverting the Hessian of the loss function. We use a variant of a new conditional independence coefficient, L-CODEC, to identify a subset of the model parameters with the most semantic overlap on an individual sample level. Our approach completely avoids the need to invert a (possibly) huge matrix. By utilizing a Markov blanket selection common in the literature, we premise that L-CODEC is also suitable for deep unlearning, as well as other applications in vision. Compared to alternatives, L-CODEC makes approximate unlearning possible in settings that would otherwise be infeasible, including vision models used for face recognition, person re-identification and NLP models that may require unlearning data identified for exclusion.

Full Paper Link at CVPR 2022 Proceedings.

Supplementary Material

Code

All experiments are run within the specified folders, and call out to 'codec'. Navigate to each folder for example scripts and directions on how to run in expname/README.md.

Conditional Independence Core

For our core conditional independence testing engine, you can check out and use the functions in the codec/ folder. We provide MATLAB and Python implementations of the newly proposed measure of Conditional Dependence (CODEC) and the associated feature selection scheme FOCI. Additionally, it also has the implementation of our proposed randomized versions of LCODEC and LFOCI. There is significant gain in computation time when nearest neighbors are computed on GPU as done in our implementations.

Deep Learning Pipeline

For the deep learning unlearning pipeline, the scrub/scrub_tools.py file contains the main procedure. Our input perturbation revolves around the following at lines 145 and 188-192:

for m in range(params.n_perturbations):
	tmpdata = x + (0.1)*torch.randn(x.shape).to(device)
	acts, out = myActs.getActivations(tmpdata.to(device))
	loss = criterion(out, y_true)
	vec_acts = p2v(acts)

where the getActivations is computed using PyTorch activation hooks defined in scrub/hypercolumn.py.

Reference

If you find our paper helpful and use this code, please cite our publication at CVPR 2022.

@InProceedings{Mehta_2022_CVPR,
    author    = {Mehta, Ronak and Pal, Sourav and Singh, Vikas and Ravi, Sathya N.},
    title     = {Deep Unlearning via Randomized Conditionally Independent Hessians},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {10422-10431}
}