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View Code? Open in Web Editor NEWOfficial repository for the paper "Topological Neural Discrete Representation Learning à la Kohonen" (ICML 2023 Workshop on Sampling and Optimization in Discrete Space)
Official repository for the paper "Topological Neural Discrete Representation Learning à la Kohonen" (ICML 2023 Workshop on Sampling and Optimization in Discrete Space)
It seems that in som_vector_quantizer.py, in class HardSOM, in the method "update", the embedding weights are not updated correctly: on line 202: "self._weight = self._ema_w / self._ema_cluster_size.unsqueeze(1)" should be replaced by " self._w = self._ema_w / self._ema_cluster_size.unsqueeze(1)". The variable "self._weight" is used nowhere else in the code, I expect this should be self._w. After fixing this bug, I'm able to get good quality reconstructions using my own VQ-VAE implementation that uses the HardSOM as a quantizer layer. Without the fix, the model underperforms drastically.
Thank you for sharing this code, I believe the Kohonen SOM is a valuable alternative for the EMA updating.
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