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Texas A&M University, City University of Hong Kong
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Kullback鈥揕eibler divergence is the secret sauce that ensures density-level and sample-conditioned objectives align perfectly in generative modeling.
Stealing frequency-awareness from State-Space Models lets image tokenizers generate higher-quality images without sacrificing compression.
