Data relativistic uncertainty framework for low-illumination anime scenery image enhancement

arXiv:2512.21944 · cs.CV, cs.LG, cs.MM · Submitted 2025-12-26 · Read on arXiv

cs.CV, cs.LG, cs.MM

Submitted: 2025-12-26

Updated: 2026-03-11

Code: https://github.com/StudioYG/DRU_framework

Terminology

• Data Relativistic Uncertainty — 9× in this paper
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• The relativistic discriminator: a key element missing from standard GAN
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks

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