钢盔
发表于 2025-3-25 03:42:30
Örn B. Bodvarsson,Hendrik Van den Berggmentation of an image from the previous epoch, and (2) outperforms PAWS in semi-supervised setting with less training resources when the constraint ensures that the NNs have the same pseudo-label as the query. Our code is available here: ..
调味品
发表于 2025-3-25 09:38:33
Economic Growth and Immigrationto work well under the linear evaluation protocol, while may hurt the transfer performances on long-tailed classification tasks. Moreover, negative samples do not make models more sensible to the choice of data augmentations, nor does the asymmetric network structure. We believe our findings provide useful information for future work.
amygdala
发表于 2025-3-25 13:35:37
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BOON
发表于 2025-3-25 18:25:17
The Supply Curve Under Perfect Competition the virtual category as the lower bound of the inter-class distance. Moreover, we also modify the localisation loss to allow high-quality boundaries for location regression. Extensive experiments demonstrate that the proposed VC learning significantly surpasses the state-of-the-art, especially with small amounts of available labels.
committed
发表于 2025-3-25 20:53:53
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BAIL
发表于 2025-3-26 03:44:40
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恶臭
发表于 2025-3-26 05:23:55
,Constrained Mean Shift Using Distant yet Related Neighbors for Representation Learning,gmentation of an image from the previous epoch, and (2) outperforms PAWS in semi-supervised setting with less training resources when the constraint ensures that the NNs have the same pseudo-label as the query. Our code is available here: ..
烦忧
发表于 2025-3-26 10:29:24
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Deference
发表于 2025-3-26 15:09:38
Data Invariants to Understand Unsupervised Out-of-Distribution Detection, on the invariants of the training dataset. We show how this characterization is unknowingly embodied in the top-scoring MahaAD method, thereby explaining its quality. Furthermore, our approach can be used to interpret predictions of U-OOD detectors and provides insights into good practices for evaluating future U-OOD methods.
obstruct
发表于 2025-3-26 16:51:32
Semi-supervised Object Detection via VC Learning, the virtual category as the lower bound of the inter-class distance. Moreover, we also modify the localisation loss to allow high-quality boundaries for location regression. Extensive experiments demonstrate that the proposed VC learning significantly surpasses the state-of-the-art, especially with small amounts of available labels.