Just Align x: Aligning Predictions, Not Representations

arXiv:2610.00600 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Alignment — 66× in this paper · explained in 1 episode
• Consistency — 17× in this paper · explained in 3 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Generative Model — 7× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Generative Modeling — 5× in this paper · explained in 3 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 5× in this paper
• Alignment Methods — 4× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Flow Matching — 3× in this paper · explained in 8 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• In European Conference on Computer Vision — 2× in this paper
• International Conference on Computer Vision — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Distillation — 1× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 1× in this paper
• Inception Score — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes

Sources

• Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation
• Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis
• Asymmetric Flow Models
• PixelFlow: Pixel-Space Generative Models with Flow
• Classifier-Free Diffusion Guidance
• Efficient Generative Model Training via Embedded Representation Warmup
• Improved Baselines with Representation Autoencoders
• SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training
• Diffuse and Disperse: Image Generation with Representation Regularization
• Stable Velocity: A Variance Perspective on Flow Matching

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models