EEGDancer integrates VQ-VAE latent space learning, masked Transformer modeling, and SAC reinforcement learning to improve continuous EEG emotion prediction over prior methods on SEED datasets.
Discrete representations strengthen vision transformer robustness.arXiv preprint arXiv:2111.10493
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ArcVQ-VAE adds spherical angular-margin regularization consisting of ball-bounded norms and arc-cosine margin loss to improve codebook utilization in VQ-VAE for image tasks.
citing papers explorer
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EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction
EEGDancer integrates VQ-VAE latent space learning, masked Transformer modeling, and SAC reinforcement learning to improve continuous EEG emotion prediction over prior methods on SEED datasets.
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ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin
ArcVQ-VAE adds spherical angular-margin regularization consisting of ball-bounded norms and arc-cosine margin loss to improve codebook utilization in VQ-VAE for image tasks.