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Found 3 papers, 2 papers with code
Selecting task with optimal transport self-supervised learning for few-shot classification
Few-Shot classification aims at solving problems that only a few samples are available in the training process.
Semantic-Preserving Adversarial Text Attacks
In this paper, we propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models.
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition
To alleviate these problems, this paper proposes the targeted attention attack (TAA) method for real world road sign attack.
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