3 papers
cs.LG2026
Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
Duncan Soiffer, Chandler Squires, Yuan Guan +2
The task of compositional generation involves using a conditional generative model, trained only on a subset of the possible conditions, to produce samples from compositionally-def…
cs.LG2025
Learning What Matters: Steering Diffusion via Spectrally Anisotropic Forward Noise
Luca Scimeca, Thomas Jiralerspong, Berton Earnshaw +2
Diffusion Probabilistic Models (DPMs) have achieved strong generative performance, yet their inductive biases remain largely implicit. In this work, we aim to build inductive biase…
cs.LG2025
Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
Thomas Jiralerspong, Berton Earnshaw, Jason Hartford +2
Diffusion Probabilistic Models (DPMs) are powerful generative models that have achieved unparalleled success in a number of generative tasks. In this work, we aim to build inductiv…