VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers
Training quantization modules for leading VQ models costs enough compute that new techniques rarely get tested outside well-funded labs. This ICLR 2026 paper offers a way around that. VQ-Transplant swaps a new quantization module into a frozen pre-trained tokenizer, keeping all encoder and decoder parameters intact. A short decoder adaptation step closes the resulting mismatch. The result reaches near state of the art reconstruction at 95 percent lower training cost.
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