{"total":17,"items":[{"citing_arxiv_id":"2607.00647","ref_index":11,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Not All Prediction Targets Keep Training-Free Diffusion Guidance on the Manifold","primary_cat":"cs.CV","submitted_at":"2026-07-01T09:01:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"x-prediction maintains manifold adherence during training-free diffusion guidance better than ε- or v-prediction, per theoretical analysis and experiments on bird classification and style transfer.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.31147","ref_index":8,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"WaterGen: Decoupling Scene and Medium in Underwater Image 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enabling Jacobian-free on-manifold edits in diffusion models via alternating tangent steps and diffusion projections.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.23888","ref_index":51,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Model","primary_cat":"cs.LG","submitted_at":"2026-04-26T21:23:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Geometry-preserving losses based on tangent-space distances improve blackbox GAN adaptation to shifted distributions compared with standard losses.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22942","ref_index":5,"ref_count":2,"confidence":0.55,"is_internal_anchor":false,"paper_title":"VS-DDPM: Efficient Low-Cost 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However, when using limited-capacity generative models, the synthesized content often lacks realism or fine details. 2.3 Generative HDR Advancesingenerativemodeling,includingGANs[4,9,10,22,40,48-50,79,83,106] and diffusion models [3,16,31,34,39,67,74,88-90,96,102,105,107,108,112,113], have shown strong priors for image and video generation. Some approaches learn themappingfromLDRimagestoHDRusingonlyLDRvideos,withoutrequiring HDR supervision [5]. Similarly, GlowGAN [85] enables GAN-based HDR image generation by learning from the distribution of LDR content. Diffusion models, in particular, have demonstrated strong capability in gen-"},{"citing_arxiv_id":"2602.10764","ref_index":5,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Dual-End Consistency Model","primary_cat":"cs.CV","submitted_at":"2026-02-11T11:51:01+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DE-CM trains a flow-map consistency model on three sub-trajectories (coupling, instantaneous, noise-to-noisy) and reports 1.70 FID one-step on ImageNet 256.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}