DualFashion introduces a dual-diffusion Transformer with image and text branches that generates both visual items and semantic descriptions for explainable personalized fashion recommendation.
Using human feedback to fine-tune diffusion models without any reward model
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VASR separates continuation and residual variance in reward-guided diffusion SMC, using optimal mass allocation and systematic resampling to achieve up to 26% better FID scores and faster runtimes than prior SMC and MCTS methods.
BalancedDPO applies majority-vote consensus from multiple preference scorers and dynamic reference model updates within DPO to achieve multi-metric alignment for text-to-image diffusion models, reporting improved win rates on Pick-a-Pic, PartiPrompt, and HPD datasets across SD 1.5, 2.1, and SDXL.
citing papers explorer
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Dual-Diffusional Generative Fashion Recommendation
DualFashion introduces a dual-diffusion Transformer with image and text branches that generates both visual items and semantic descriptions for explainable personalized fashion recommendation.
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VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion
VASR separates continuation and residual variance in reward-guided diffusion SMC, using optimal mass allocation and systematic resampling to achieve up to 26% better FID scores and faster runtimes than prior SMC and MCTS methods.
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BalancedDPO: Adaptive Multi-Metric Alignment
BalancedDPO applies majority-vote consensus from multiple preference scorers and dynamic reference model updates within DPO to achieve multi-metric alignment for text-to-image diffusion models, reporting improved win rates on Pick-a-Pic, PartiPrompt, and HPD datasets across SD 1.5, 2.1, and SDXL.