How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control

• URL: http://arxiv.org/abs/2302.03791v3
• Date: Wed, 27 Dec 2023 14:48:05 GMT
• Title: How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
• Authors: Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman, Jeremias Sulam
• Abstract summary: We focus on image-to-image regression tasks and we present a generalization of the Risk-Controlling Prediction Sets (RCPS) procedure. Ours relies on a novel convex optimization approach that allows for multidimensional risk control while provably minimizing the mean interval length. We illustrate our approach on two real-world image denoising problems: on natural images of faces as well as on computed tomography (CT) scans of the abdomen.
• Score: 9.811982443156063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks. While they provide high-quality and diverse samples from empirical distributions, important questions remain on the reliability and trustworthiness of these sampling procedures for their responsible use in critical scenarios. Conformal prediction is a modern tool to construct finite-sample, distribution-free uncertainty guarantees for any black-box predictor. In this work, we focus on image-to-image regression tasks and we present a generalization of the Risk-Controlling Prediction Sets (RCPS) procedure, that we term $K$-RCPS, which allows to $(i)$ provide entrywise calibrated intervals for future samples of any diffusion model, and $(ii)$ control a certain notion of risk with respect to a ground truth image with minimal mean interval length. Differently from existing conformal risk control procedures, ours relies on a novel convex optimization approach that allows for multidimensional risk control while provably minimizing the mean interval length. We illustrate our approach on two real-world image denoising problems: on natural images of faces as well as on computed tomography (CT) scans of the abdomen, demonstrating state of the art performance.

Related papers

• Robust Representation Consistency Model via Contrastive Denoising [83.47584074390842]
  randomized smoothing provides theoretical guarantees for certifying robustness against adversarial perturbations. diffusion models have been successfully employed for randomized smoothing to purify noise-perturbed samples. We reformulate the generative modeling task along the diffusion trajectories in pixel space as a discriminative task in the latent space.
  arXiv  Detail & Related papers  (2025-01-22T18:52:06Z)
• InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models [10.213390634031049]
  Current IIL methods face two main challenges: a tendency towards overconfidence and difficulty in detecting subtle tampering boundaries. We propose a new paradigm that treats IIL as a conditional mask generation task utilizing diffusion models. Our method, InpDiffusion, utilizes the denoising process enhanced by the integration of image semantic conditions to progressively refine predictions.
  arXiv  Detail & Related papers  (2025-01-06T07:32:12Z)
• Perturb, Attend, Detect and Localize (PADL): Robust Proactive Image Defense [5.150608040339816]
  We introduce PADL, a new solution able to generate image-specific perturbations using a symmetric scheme of encoding and decoding based on cross-attention. Our method generalizes to a range of unseen models with diverse architectural designs, such as StarGANv2, BlendGAN, DiffAE, StableDiffusion and StableDiffusionXL.
  arXiv  Detail & Related papers  (2024-09-26T15:16:32Z)
• Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization [59.758009422067]
  We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. We propose a new uncertainty Bellman equation (UBE) whose solution converges to the true posterior variance over values. We introduce a general-purpose policy optimization algorithm, Q-Uncertainty Soft Actor-Critic (QU-SAC) that can be applied for either risk-seeking or risk-averse policy optimization.
  arXiv  Detail & Related papers  (2023-12-07T15:55:58Z)
• Uncertainty Quantification via Neural Posterior Principal Components [26.26693707762823]
  Uncertainty quantification is crucial for the deployment of image restoration models in safety-critical domains. We present a method for predicting the PCs of the posterior distribution for any input image, in a single forward pass of a neural network. Our method reliably conveys instance-adaptive uncertainty directions, achieving uncertainty quantification comparable with posterior samplers.
  arXiv  Detail & Related papers  (2023-09-27T09:51:29Z)
• Denoising Diffusion Restoration Models [110.1244240726802]
  Denoising Diffusion Restoration Models (DDRM) is an efficient, unsupervised posterior sampling method. We demonstrate DDRM's versatility on several image datasets for super-resolution, deblurring, inpainting, and colorization.
  arXiv  Detail & Related papers  (2022-01-27T20:19:07Z)
• Deblurring via Stochastic Refinement [85.42730934561101]
  We present an alternative framework for blind deblurring based on conditional diffusion models. Our method is competitive in terms of distortion metrics such as PSNR.
  arXiv  Detail & Related papers  (2021-12-05T04:36:09Z)
• Robustness via Uncertainty-aware Cycle Consistency [44.34422859532988]
  Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs. Existing methods learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty. We propose a novel probabilistic method based on Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC)
  arXiv  Detail & Related papers  (2021-10-24T15:33:21Z)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)
• Uncertainty-aware Generalized Adaptive CycleGAN [44.34422859532988]
  Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner. Existing methods often learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty. We propose a novel probabilistic method called Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC)
  arXiv  Detail & Related papers  (2021-02-23T15:22:35Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.