Date of Award

6-1-2020

Document Type

Thesis (Undergraduate)

Department

Department of Computer Science

First Advisor

Lorenzo Torresani

Abstract

We propose an algorithm for training neural networks in noisy label scenarios that up-weighs per-example gradients that are more similar to other gradients in the same minibatch. Our approach makes no assumptions about the amount or type of label noise, does not use a held-out validation set of clean examples, makes relatively few computations, and only modifies the minibatch gradient aggregation module in a typical neural network training workflow. For CIFAR-10 classification with varying levels of label noise, our method successfully up-weighs clean examples and de-prioritizes noisy examples, showing consistent improvement over a vanilla training baseline. Our results open the door to potential future work involving per-example gradient comparisons.

Comments

Originally posted in the Dartmouth College Computer Science Technical Report Series, number TR2020-899.

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