DDCL-Attention introduces a collapse-free prototype readout for transformers that decomposes the training loss exactly into reconstruction and diversity terms while providing stability guarantees via singular perturbation theory.
Addressing representation collapse in vector quan- tized models with one linear layer.Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
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Collapse-Free Prototype Readout Layer for Transformer Encoders
DDCL-Attention introduces a collapse-free prototype readout for transformers that decomposes the training loss exactly into reconstruction and diversity terms while providing stability guarantees via singular perturbation theory.