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Angular Visual Hardness

Chen, Beidi and Liu, Weiyang and Garg, Animesh and Yu, Zhiding and Shrivastava, Anshumali and Kautz, Jan and Anandkumar, Anima (2019) Angular Visual Hardness. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200109-084932688

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Abstract

Although convolutional neural networks (CNNs) are inspired by the mechanisms behind human visual systems, they diverge on many measures such as ambiguity or hardness. In this paper, we make a surprising discovery: there exists a (nearly) universal score function for CNNs whose correlation is statistically significant than the widely used model confidence with human visual hardness. We term this function as angular visual hardness (AVH) which is given by the normalized angular distance between a feature embedding and the classifier weights of the corresponding target category in a CNN. We conduct an in-depth scientific study. We observe that CNN models with the highest accuracy also have the best AVH scores. This agrees with an earlier finding that state-of-art models tend to improve on the classification of harder training examples. We find that AVH displays interesting dynamics during training: it quickly reaches a plateau even though the training loss keeps improving. This suggests the need for designing better loss functions that can target harder examples more effectively. Finally, we empirically show significant improvement in performance by using AVH as a measure of hardness in self-training methods for domain adaptation.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1912.02279arXivDiscussion Paper
Record Number:CaltechAUTHORS:20200109-084932688
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200109-084932688
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100577
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:09 Jan 2020 19:47
Last Modified:09 Jan 2020 19:47

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