GEAR jointly trains VQ tokenizer and AR generator end-to-end via dual hard/soft read-out and representation alignment, achieving up to 10x faster ImageNet gFID convergence than LlamaGen-REPA while generalizing across quantizers and to text-to-image.
Notes on continuous stochastic phenomena.Biometrika, 37(1/2):17–23, 1950
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Literature review catalogs geographic biases in AI from training data imbalances to generative outputs over-favoring prototypical places and discusses diversity evaluations.
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GEAR: Guided End-to-End AutoRegression for Image Synthesis
GEAR jointly trains VQ tokenizer and AR generator end-to-end via dual hard/soft read-out and representation alignment, achieving up to 10x faster ImageNet gFID convergence than LlamaGen-REPA while generalizing across quantizers and to text-to-image.
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Geographic Bias and Diversity in AI Evaluation
Literature review catalogs geographic biases in AI from training data imbalances to generative outputs over-favoring prototypical places and discusses diversity evaluations.