A Spectral Perspective of Neural Networks Robustness to Label Noise

Mär 28, 2022

Sprecher:innen

Über

Deep networks usually require a massive amount of labeled data for their training. Yet, such data may include some mistakes in the labels. Interestingly, networks have been shown to be robust to such errors. This work uses spectral analysis of their learned mapping to provide an explanation for their robustness. In particular, we relate the smoothness regularization that usually exists in conventional training to the attenuation of high frequencies, which mainly characterize noise. By using a connection between the smoothness and the spectral norm of the network weights, we suggest that one may further improve robustness via spectral normalization. Empirical experiments validate our claims and show the advantage of this normalization for classification with label noise.

Organisator

Über AISTATS 2022

AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas. Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. We encourage the submission of all papers which are in keeping with this objective at AISTATS.

Präsentation speichern

Soll diese Präsentation für 1000 Jahre gespeichert werden?

Wie speichern wir Präsentationen?

Ewigspeicher-Fortschrittswert: 0 = 0.0%

Freigeben

Empfohlene Videos

Präsentationen, deren Thema, Kategorie oder Sprecher:in ähnlich sind

Interessiert an Vorträgen wie diesem? AISTATS 2022 folgen