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Learning from Data with Noisy Labels Using Temporal Self-Ensemble
By combining the aforementioned metrics, we present the proposed {\it self-ensemble-based robust training} (SRT) method, which can filter the samples with noisy labels to reduce their influence on training.
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning
The student models are given a fixed number of temporal self-ensemble models, and the teacher model is constructed by averaging the weights of the student models.
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition
There is growing interest in the challenging visual perception task of learning from long-tailed class distributions.
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