• Corpus ID: 227054413

An Experimental Study of Semantic Continuity for Deep Learning Models

@article{Wu2020AnES,
  title={An Experimental Study of Semantic Continuity for Deep Learning Models},
  author={Shangxi Wu and Jitao Sang and Xian Zhao and Lizhang Chen},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.09789}
}
Deep learning models suffer from the problem of semantic discontinuity: small perturbations in the input space tend to cause semantic-level interference to the model output. We argue that the semantic discontinuity results from these inappropriate training targets and contributes to notorious issues such as adversarial robustness, interpretability, etc. We first conduct data analysis to provide evidence of semantic discontinuity in existing deep learning models, and then design a simple… 

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