Collective Decision of One-vs-Rest Networks for Open Set Recognition
- URL: http://arxiv.org/abs/2103.10230v2
- Date: Fri, 19 Mar 2021 22:00:04 GMT
- Title: Collective Decision of One-vs-Rest Networks for Open Set Recognition
- Authors: Jaeyeon Jang and Chang Ouk Kim
- Abstract summary: We propose a simple open set recognition (OSR) method based on the intuition that OSR performance can be maximized by setting strict and sophisticated decision boundaries.
The proposed method performed significantly better than the state-of-the-art methods by effectively reducing overgeneralization.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unknown examples that are unseen during training often appear in real-world
machine learning tasks, and an intelligent self-learning system should be able
to distinguish between known and unknown examples. Accordingly, open set
recognition (OSR), which addresses the problem of classifying knowns and
identifying unknowns, has recently been highlighted. However, conventional deep
neural networks using a softmax layer are vulnerable to overgeneralization,
producing high confidence scores for unknowns. In this paper, we propose a
simple OSR method based on the intuition that OSR performance can be maximized
by setting strict and sophisticated decision boundaries that reject unknowns
while maintaining satisfactory classification performance on knowns. For this
purpose, a novel network structure is proposed, in which multiple one-vs-rest
networks (OVRNs) follow a convolutional neural network feature extractor. Here,
the OVRN is a simple feed-forward neural network that enhances the ability to
reject nonmatches by learning class-specific discriminative features.
Furthermore, the collective decision score is modeled by combining the multiple
decisions reached by the OVRNs to alleviate overgeneralization. Extensive
experiments were conducted on various datasets, and the experimental results
showed that the proposed method performed significantly better than the
state-of-the-art methods by effectively reducing overgeneralization.
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