mlsanityData to 7 Oct 2026

Class Interference of Deep Neural Networks

arXiv 2211.01370 ↗First posted 31 Oct 2022cs.LG cs.AI cs.CVcited by 1

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Abstract (arXiv)

Recognizing and telling similar objects apart is even hard for human beings. In this paper, we show that there is a phenomenon of class interference with all deep neural networks. Class interference represents the learning difficulty in data, and it constitutes the largest percentage of generalization errors by deep networks. To understand class interference, we propose cross-class tests, class ego directions and interference models. We show how to use these definitions to study minima flatness and class interference of a trained model. We also show how to detect class interference during training through label dancing pattern and class dancing notes.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 31 Oct 2022.

Builds on

18 of 26 references.

References · 18

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6 more
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Citing papers per year: 2022: 1.

1 paper.

  1. 2022The Vanishing Decision Boundary Complexity and the Strong First Componentcited by 0