科研进展：修订间差异
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#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Liu Z., etc. Understanding Data Augmentation from a Robustness Perspective. In '''''Proceedings of ICASSP 2024''''', Accepted</span></span> | #<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Liu Z., etc. Understanding Data Augmentation from a Robustness Perspective. In '''''Proceedings of ICASSP 2024''''', Accepted</span></span> | ||
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Liu Z., etc. From Game Theory to Visual Recognition: Advancing DNN Robustness. In '''''Proceedings of ICASSP 2024''''', Accepted</span></span> | #<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Liu Z., etc. From Game Theory to Visual Recognition: Advancing DNN Robustness. In '''''Proceedings of ICASSP 2024''''', Accepted</span></span> | ||
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Jiang W., etc. EDOS: Diverse Outlier Sampling for Out-of-Distribution Detection. In '''''Proceedings of ICLR 2024''''', Accepted</span></span> | |||
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">CASE: Exploiting Intra-class Compactness and Inter-class Separability of Feature Embeddings for Out-of-Distribution Detection. In '''''Proceedings of AAAI 2024''''', Accepted</span></span> | #<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">CASE: Exploiting Intra-class Compactness and Inter-class Separability of Feature Embeddings for Out-of-Distribution Detection. In '''''Proceedings of AAAI 2024''''', Accepted</span></span> | ||
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants. In '''''Proceedings of AAAI 2024''''', Accepted</span></span> | #<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants. In '''''Proceedings of AAAI 2024''''', Accepted</span></span> | ||