极少 发表于 2025-3-30 10:33:41
,Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining,torative learning, and finally, the pretrained encoder-decoder is associated with an adversarial encoder for final full discriminative, restorative, and adversarial learning. Our extensive experiments demonstrate that the stepwise incremental pretraining stabilizes United models training, resultingApogee 发表于 2025-3-30 14:24:11
http://reply.papertrans.cn/29/2825/282481/282481_52.png轮流 发表于 2025-3-30 16:45:22
,Seamless Iterative Semi-supervised Correction of Imperfect Labels in Microscopy Images,ssfully provides an adaptive early learning correction technique for object detection. The combination of early learning correction that has been applied in classification and semantic segmentation before and synthetic-like image generation proves to be more effective than the usual semi-supervisedCatheter 发表于 2025-3-31 00:14:44
,Adaptive Optimization with Fewer Epochs Improves Across-Scanner Generalization of U-Net Based Medic, by training for only 50 epochs with AvaGrad, and to exceed their results in the across-scanner setting. This benefit is specific to combining adaptive optimization and early stopping, since it can be matched neither by SGD with a low number of epochs, nor by Avagrad with many epochs. Finally, we d离开就切除 发表于 2025-3-31 00:58:52
Konstantinos Kamnitsas,Lisa Koch,Sotirios Tsaftaristress-response 发表于 2025-3-31 05:38:11
Preston K. Kerr,Steven B. Brandes imaging: (1) good initialization is more crucial for transformer-based models than for CNNs, (2) self-supervised learning based on masked image modeling captures more generalizable representations than supervised models, and (3) assembling a larger-scale domain-specific dataset can better bridge th收集 发表于 2025-3-31 10:51:39
Treatment of the Ureteral Lesionsults show that our proposed volumetric task definition leads to up to . improvement in terms of IoU compared to related baselines. The proposed update rule is also shown to improve the performance for complex scenarios where the data distribution of the target organ is very different from the sourc