Winner-Take-All bottlenecks enforce disentangled symbolic representations in multi-task learning
Winner-take-all (WTA) networks constitute a central circuit motif in cortical networks of the brain. In addition, WTA-like activations are abundant in modern deep learning models in the form of the softmax activation for example in attention layers of transformers. While their role in the extraction of latent factors has been studied for relatively simple generative models, their role in the context of highly non-linearly entangled latent factors has remained elusive. In this article, we show that a WTA bottleneck within a deep neural network can enforce under certain well-defined conditions the extraction of categorical latent factors of the data in a multi-task learning setup. In partic...
| Authors | Julian Gutheil, Simon Hitzginger, Robert Legenstein |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 2 July 2026 |
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