GAVEL
发表于 2025-3-30 11:41:52
Gerd Melkus,Kawan S. Rakhraassessing and comparing the robustness of trained models. Furthermore, we characterize scale as a way to distinguish small and large perturbations, and relate it to inherent properties of data sets, demonstrating that robustness thresholds must be chosen accordingly. We hope that our work contribute
善变
发表于 2025-3-30 15:48:16
Kamal Bali,Stéphane Poitras,Sasha Carsen the performance of the different models and kernels. Our results reveal interesting findings. For instance, we find that theoretically more powerful models do not necessarily yield higher-quality representations, while graph kernels are shown to be very competitive with graph neural networks.
内向者
发表于 2025-3-30 16:55:52
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ELATE
发表于 2025-3-30 21:59:58
Geoffrey P. Wilkinpects. Evaluation results show that our method outperforms others in terms of sentence fluency and achieves a decent tradeoff between content preservation and style transfer intensity. The superior performance on the Caption dataset illustrates our method’s potential advantage on occasions of limite
怕失去钱
发表于 2025-3-31 01:36:26
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vertebrate
发表于 2025-3-31 06:24:26
Yuri A. Pompeu,Ernest Sinkd while highlighting the weak features. In addition, considering the different responses of channels to output, we present a weighted aggregation block (WAB) to strengthen the significant channel features and recalibrate channel-wise feature responses. Extensive experiments on five benchmark dataset
ADORE
发表于 2025-3-31 11:01:07
Etienne L. Belzile,Antoine Bureau,Maged Shahinduring the feature transfer and refine the object edges. Finally, these three modules are merged into a unified and end-to-end network to predict the fine-grained boundary-preserving salient objects. Experimental results on three prevailing benchmarks show that our MineNet outperforms other competit