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This paper presents a novel sampling scheme for masked non-autoregressivegenerative modeling. We identify the limitations of TimeVQVAE, MaskGIT, andToken-Critic in their sampling processes, and propose Enhanced Sampling Scheme(ESS) to overcome these limitations. ESS explicitly ensures both samplediversity and fidelity, and consists of three stages: Naive Iterative Decoding,Critical Reverse Sampling, and Critical Resampling. ESS starts by sampling atoken set using the naive iterative decoding as proposed in MaskGIT, ensuringsample diversity. Then, the token set undergoes the critical reverse sampling,masking tokens leading to unrealistic samples. After that, critical resamplingreconstructs masked tokens until the final sampling step is reached to ensurehigh fidelity. Critical resampling uses confidence scores obtained from aself-Token-Critic to better measure the realism of sampled tokens, whilecritical reverse sampling uses the structure of the quantized latent vectorspace to discover unrealistic sample paths. We demonstrate significantperformance gains of ESS in both unconditional sampling and class-conditionalsampling using all the 128 datasets in the UCR Time Series archive.

Masked Generative Modeling with Enhanced Sampling Scheme