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Sara Hooker: It is not just the data. Popular compression techniques amplify bias in deep neural networks. Work co-led with @nunuska, w @_whatcode, Samy Bengio and @cephaloponderer "Characterizing the bias of compressed models" -- pre-print -

10 replies, 418 likes

Vitaly Feldman: Nice to see the experiments ( follow the predictions of theory ( for once. Limiting memorization (such as in model compression) has a larger effect on the accuracy of lower-frequency subpopulations.

1 replies, 81 likes

Kyunghyun Cho: perhaps not surprising, but it's certainly not what i thought of: weight pruning is not much of compression but interpolation across different solutions with different properties not captured by a usual test set.

2 replies, 52 likes

Raym Geis: Bias from data compression. β€œThe network is remarkably tolerant of high levels of compression, but cannibalizes performances on underrepresented features in order to preserve top-line metrics.”

0 replies, 25 likes

Matthew Fenech: Deep NNs are often compressed to deal with resource constraints. Great paper here showing that resulting errors disproportionately affect underrepresented parts of dataset, introducing bias. It's not just the data - every choice made by development teams has consequences.

1 replies, 18 likes

Neil Thompson: Discouraging (but important) news: some of the techniques for decreasing the computational burden of deep learning can increase bias.

0 replies, 7 likes

Vikash Sehwag: Interesting! Pruning has a disparate impact on the accuracy of different classes. Great to see increasingly more focus on the impact of pruning on different performance metrics.

1 replies, 6 likes

Rohit Pgarg: Model compression/quantization/pruning disproportionately impacts the unusual training samples.

0 replies, 6 likes

Cody Blakeney: This is why it's very important to preserve not just the accuracy but the decision boundary and internal representations. Pruning is not compression.

0 replies, 2 likes

Simone Scardapane: Model compression amplifies model bias on the CelebA dataset. πŸ‘‡ Makes a lot of sense upon reflection: if you have constraints and want to preserve an average metric, it's easiest to let go of the "end of the tail". Looking forward to "fairness-aware compression" in the future!

1 replies, 2 likes


Found on Oct 13 2020 at

PDF content of a computer science paper: CHARACTERISING BIAS IN COMPRESSED MODELS