• Corpus ID: 240353692

Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations: late-stopping, tuning batch normalization and invariance loss

@article{Sakai2021ThreeAT,
  title={Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations: late-stopping, tuning batch normalization and invariance loss},
  author={A. Sakai and Taro Sunagawa and Spandan Madan and Kanata Suzuki and Takashi Katoh and Hiromichi Kobashi and Hanspeter Pfister and Pawan Sinha and Xavier Boix and Tomotake Sasaki},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.00131}
}
The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available. In this paper, we investigate three different approaches to improve DNNs in recognizing objects in OoD orientations and illuminations… 
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