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Correction Training for Diffusion Models
Donghoon Ahn, Runqian Wang, Alexei A. Efros +2
Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with noise, but at inference its inp…
Base Models Can Reason By Taking a Cue From Training Data
Sophie L. Wang, Amil Dravid, Rulin Shao +3
In this paper, we study how training data creates associations between the tokens at the start of a base model's response and the reasoning behavior that follows. First, we demonst…
MosaiChunk: Compositing Spatio-Temporal Memory for Autoregressive Video Generation
Yiwen Zhang, Haocheng Xi, Michael Tian-Yue Liu +4
Long-horizon autoregressive video generation is limited by a finite context window. When an object or scene falls out of context, its fine-grained visual details may be lost and di…
Bottom-up Modeling of Repeated Elements via Single Image Analysis-by-Synthesis
Syrine Kalleli, Alexei A. Efros, Mathieu Aubry
We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated datasets, curated multi-image collect…
Balancing Frequencies and Pixels in Flow Matching
Lucas Degeorge, Paul Couairon, Arijit Ghosh +3
Natural images follow a spectral distribution: most signal energy lies in the low spatial frequencies, while the perceptually important structures such as textures and edge…
The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric
Sheng-Yu Wang, Yotam Nitzan, Aaron Hertzmann +4
Human visual similarity judgments are context-dependent. For example, two images may be similar in shape but distinct in color. Existing perceptual similarity metrics, however, col…
Autoregressive Modeling of Film with Applications in Video Montage
Marcelo Sandoval-Castañeda, Fabian Caba Heilbron, Shiry Ginosar +4
This work introduces FilmGPT, an autoregressive transformer designed to address the challenge of video montage--turning a collection of raw, "unwatchable" footage into coherent cin…
The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation
Nicolas Dufour, Alexei A. Efros, Patrick Pérez
The Frechet Inception Distance (FID) is the de facto arbiter of image generation, yet most papers report just a single number from a single trained model using a single sampling se…
Neuron Populations Exhibit Divergent Selectivity with Scale
Amil Dravid, Yasaman Bahri, Alexei A. Efros +1
We investigate whether neuron populations within neural networks evolve predictably with scale, extending scaling laws beyond macroscopic observables such as loss. To probe this qu…
Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
A. Sophia Koepke, Daniil Zverev, Shiry Ginosar +1
The Platonic Representation Hypothesis posits that neural networks trained on different modalities (e.g., text and images) converge toward a shared representation of reality. If tr…
It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models
Anne Harrington, A. Sophia Koepke, Shyamgopal Karthik +2
Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prompt. Previous work has attempted…