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Found 4 papers, 1 papers with code
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation
To this end, at every generator update, we aim to maintain the student's performance on previously encountered examples while acquiring knowledge from samples of the current distribution.
Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition
To this end, we propose a pseudo-label generation and an uncertainty-based data selection framework for semi-supervised text recognition.
Weakly supervised segmentation with cross-modality equivariant constraints
In addition, we add a KL-divergence on the class prediction distributions to facilitate the information exchange between modalities, which, combined with the equivariant regularizers further improves the performance of our model.
Switching Loss for Generalized Nucleus Detection in Histopathology
Remarkably, without retraining on target datasets, our pre-trained nucleus detector also outperformed existing nucleus detectors that were trained on at least some of the images from the target datasets.
