SSOCL assigns pseudo-labels to unlabeled EEG streams via clustering of predicted embeddings and trains the model with an entropy-filtered replay buffer, claiming state-of-the-art cross-subject emotion recognition.
A simple framework for contrastive learning of visual representations, in: Pro- ceedings of the 37th International Conference on Machine Learning, pp
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Robust Emotion Recognition via Bi-Level Self-Supervised Continual Learning
SSOCL assigns pseudo-labels to unlabeled EEG streams via clustering of predicted embeddings and trains the model with an entropy-filtered replay buffer, claiming state-of-the-art cross-subject emotion recognition.