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In this paper, we study a simple and generic framework to tackle the problem of learning model parameters when a fraction of the training samples are corrupted. Our approach is motivated by a simple observation: in a variety of such settings, the evolution of training accuracy (as a function of training epochs) is different for clean samples and bad samples. We propose to iteratively minimize the trimmed loss, by alternating between (a) selecting samples with lowest current loss, and (b) retraining a model on only these samples. Analytically, we characterize the statistical performance and convergence rate of the algorithm for simple and natural linear and non-linear models. Experimentally, we demonstrate its effectiveness in three settings: (a) deep image classifiers with errors only in labels, (b) generative adversarial networks with bad training images, and (c) deep image classifiers with adversarial (image, label) pairs (i.e., backdoor attacks). For the well-studied setting of random label noise, our algorithm achieves state-of-the-art performance without having access to any a-priori guaranteed clean samples.
Author Information
Yanyao Shen (UT Austin)
Sujay Sanghavi (UT Austin)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: Learning with Bad Training Data via Iterative Trimmed Loss Minimization »
Wed Jun 12th 06:20 -- 06:25 PM Room Room 102
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2020 Poster: Extreme Multi-label Classification from Aggregated Labels »
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2019 Poster: Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling »
Shanshan Wu · Alexandros Dimakis · Sujay Sanghavi · Felix Xinnan Yu · Daniel Holtmann-Rice · Dmitry Storcheus · Afshin Rostamizadeh · Sanjiv Kumar -
2019 Oral: Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling »
Shanshan Wu · Alexandros Dimakis · Sujay Sanghavi · Felix Xinnan Yu · Daniel Holtmann-Rice · Dmitry Storcheus · Afshin Rostamizadeh · Sanjiv Kumar