Can overfitted deep neural networks in adversarial training generalize? – An approximation viewpoint (2024)

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Can overfitted deep neural networks in adversarial training generalize? – An approximation viewpoint 2024

Can overfitted deep neural networks in adversarial training generalize? – An approximation viewpoint 2024 - Analysis

Title: Can overfitted deep neural networks in adversarial training generalize? – An approximation viewpoint
Genre: Documentary, Crime
Country: United Kingdom
Studio: University of Warwick
Cast: Fanghui Liu, Engineering Research Building, Room 514, Yuchen Zeng
Crew: Fanghui Liu (Director), Fanghui Liu (Producer), Fanghui Liu (Writer)
Keyword:

Overview:In this talk, I will discuss whether overfitted DNNs in adversarial training can generalize from an approximation viewpoint. We prove by construction the existence of infinitely many adversarial training classifiers on over-parameterized DNNs that obtain arbitrarily small adversarial training error (overfitting), whereas achieving good robust generalization error under certain conditions concerning the data quality, well separated, and perturbation level. This construction is optimal and thus points out the fundamental limits of DNNs under adversarial training with statistical guarantees. Part of this talk comes from our recent work.

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Release: Mar 01, 2024 Runtime: 52 minutes Quality: HD IMDb: 10 / 10 by 0 users Popularity: 0.4235 Budget: $0 Revenue: $0 Language:

Watch HD Can overfitted deep neural networks in adversarial training generalize? – An approximation viewpoint

Can overfitted deep neural networks in adversarial training generalize? – An approximation viewpoint 2024

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