DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick

arXiv:2509.26469v4 Announce Type: replace Abstract: Vector quantization is common in deep models, yet its hard assignments block gradients and hinder end-to-end training. We propose DiVeQ, which treats quantization as adding an error vector that mimics the quantization distortion, keeping the forward pass hard while letting gradients flow. We also present a space-filling variant (SF-DiVeQ) that assigns input to a curve constructed by the lines connecting codewords, resulting in less quantization error and full codebook usage. Both methods train end-to-end without requiring auxiliary losses or

Source: arXiv cs.LG — read the full report at the original publisher.

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