SAGE: Salient Factor Discovery and Generation with Visual Foundation Representations

arXiv:2609.39635 · cs.CV, cs.LG · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/DCGM/ffhq-features-dataset

Terminology

• Accuracy — 19× in this paper · explained in 1 episode
• Transformer — 16× in this paper · explained in 2 episodes
• Clustering — 11× in this paper · explained in 2 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Decode — 8× in this paper · explained in 2 episodes
• Sparsity — 7× in this paper · explained in 4 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Fidelity — 5× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Linear probe — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adjusted Rand Index — 1× in this paper
• Adversarial Training — 1× in this paper · explained in 1 episode
• Alternation — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Normalized Mutual Information — 1× in this paper
• Perplexity — 1× in this paper · explained in 3 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Contrastive Variational Autoencoder Enhances Salient Features
• Learning Common and Salient Generative Factors Between Two Image Datasets
• Classifier-Free Diffusion Guidance
• SepVAE: a contrastive VAE to separate pathological patterns from healthy ones
• Automatic Discovery of Disease Subgroups by Contrasting with Healthy Controls
• DINOv3
• Improved Baselines with Representation Autoencoders
• Diff-CA: Separating Common and Salient Factors with Diffusion Models
• Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders
• Representation Learning with Contrastive Predictive Coding
• Qwen-Image Technical Report

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