Variable codebook sizes that increase along the sequence in visual tokenizers reduce generation FID scores significantly for autoregressive models on ImageNet.
Autoregressive Image Generation without Vector Quantization , url =
4 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
NEAT achieves state-of-the-art 3D molecular generation on QM9 and GEOM-Drugs via a neighborhood-guided autoregressive set transformer that ensures atom-level permutation invariance and offers a significant speed advantage.
Gazer uses MLLM feedback in two stages to diagnose semantic errors in intermediate AVM states and rewind/rectify the generation trajectory, improving alignment on compositional benchmarks without training.
BareWave develops a waveform-native flow-matching framework for direct text-to-waveform TTS using representation alignment, staged noise scheduling, and velocity-aware perceptual alignment to achieve strong zero-shot voice cloning results.
citing papers explorer
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Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation
Variable codebook sizes that increase along the sequence in visual tokenizers reduce generation FID scores significantly for autoregressive models on ImageNet.
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NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation
NEAT achieves state-of-the-art 3D molecular generation on QM9 and GEOM-Drugs via a neighborhood-guided autoregressive set transformer that ensures atom-level permutation invariance and offers a significant speed advantage.
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Training-Free Semantic Correction for Autoregressive Visual Models
Gazer uses MLLM feedback in two stages to diagnose semantic errors in intermediate AVM states and rewind/rectify the generation trajectory, improving alignment on compositional benchmarks without training.
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BareWave: Waveform-Native Flow-Matching Text-to-Speech
BareWave develops a waveform-native flow-matching framework for direct text-to-waveform TTS using representation alignment, staged noise scheduling, and velocity-aware perceptual alignment to achieve strong zero-shot voice cloning results.