FID

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Findings

• IC-1155ViT-B/16 (ImageNet-21k) fine-tuned with VPT outperforms full fine-tuning on 16 of 19 VTAB-1k tasks, with the advantage concentrated in high-task-disparity and similar-distribution scenarios and narrowing as downstream data grows [eval]
• IC-1484GP-UNIT's FID degrades under noisy inputs in reference-guided mode but paradoxically improves in latent-guided mode [eval]
• IC-1485Sketch Transformer's FID degrades from 31.49 to 404.01 under Gaussian noise at the highest tested intensity [eval]
• IC-1486HiFaceGAN's FID degrades from 34.83 to 320.41 under Gaussian noise at the highest tested intensity for face super-resolution [eval]
• IC-1487CycleGAN's FID degrades from 76.92 to 180.82 under Gaussian noise for horse-to-zebra translation [eval]
• IC-554In Stable Diffusion 1.4, 1.5, 2.0, and 3.0, parameters with the smallest absolute values (below ~10^-3) do not contribute to the generative process, and this ineffectiveness is caused by stochastic training dynamics rather than architectural redundancy. [eval]