A High-Quality Robust Diffusion Framework for Corrupted Dataset
- URL: http://arxiv.org/abs/2311.17101v2
- Date: Sun, 21 Jul 2024 03:26:17 GMT
- Title: A High-Quality Robust Diffusion Framework for Corrupted Dataset
- Authors: Quan Dao, Binh Ta, Tung Pham, Anh Tran,
- Abstract summary: In this paper, we introduce the first robust-to-outlier diffusion for generative adversarial model (GAN)
We show that our method exhibits robustness to corrupted datasets and achieves superior performance on clean datasets.
- Score: 3.7287133112262407
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Developing image-generative models, which are robust to outliers in the training process, has recently drawn attention from the research community. Due to the ease of integrating unbalanced optimal transport (UOT) into adversarial framework, existing works focus mainly on developing robust frameworks for generative adversarial model (GAN). Meanwhile, diffusion models have recently dominated GAN in various tasks and datasets. However, according to our knowledge, none of them are robust to corrupted datasets. Motivated by DDGAN, our work introduces the first robust-to-outlier diffusion. We suggest replacing the UOT-based generative model for GAN in DDGAN to learn the backward diffusion process. Additionally, we demonstrate that the Lipschitz property of divergence in our framework contributes to more stable training convergence. Remarkably, our method not only exhibits robustness to corrupted datasets but also achieves superior performance on clean datasets.
Related papers
- DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
Current perceptive models heavily depend on resource-intensive datasets.
We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability.
Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
arXiv Detail & Related papers (2024-03-20T04:58:03Z) - Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI)
In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion)
Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
arXiv Detail & Related papers (2024-02-15T18:59:18Z) - Damage GAN: A Generative Model for Imbalanced Data [1.027461951217988]
This study explores the application of Generative Adversarial Networks (GANs) within the context of imbalanced datasets.
We introduce a novel network architecture known as Damage GAN, building upon the ContraD GAN framework which seamlessly integrates GANs and contrastive learning.
arXiv Detail & Related papers (2023-12-08T06:36:33Z) - Consistency Regularization for Generalizable Source-free Domain
Adaptation [62.654883736925456]
Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset.
Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets.
We propose a consistency regularization framework to develop a more generalizable SFDA method.
arXiv Detail & Related papers (2023-08-03T07:45:53Z) - Diff-Instruct: A Universal Approach for Transferring Knowledge From
Pre-trained Diffusion Models [77.83923746319498]
We propose a framework called Diff-Instruct to instruct the training of arbitrary generative models.
We show that Diff-Instruct results in state-of-the-art single-step diffusion-based models.
Experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models.
arXiv Detail & Related papers (2023-05-29T04:22:57Z) - GSURE-Based Diffusion Model Training with Corrupted Data [35.56267114494076]
We propose a novel training technique for generative diffusion models based only on corrupted data.
We demonstrate our technique on face images as well as Magnetic Resonance Imaging (MRI)
arXiv Detail & Related papers (2023-05-22T15:27:20Z) - TWINS: A Fine-Tuning Framework for Improved Transferability of
Adversarial Robustness and Generalization [89.54947228958494]
This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks.
We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework.
TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
arXiv Detail & Related papers (2023-03-20T14:12:55Z) - Restoration based Generative Models [0.886014926770622]
Denoising diffusion models (DDMs) have attracted increasing attention by showing impressive synthesis quality.
In this paper, we establish the interpretation of DDMs in terms of image restoration (IR)
We propose a multi-scale training, which improves the performance compared to the diffusion process, by taking advantage of the flexibility of the forward process.
We believe that our framework paves the way for designing a new type of flexible general generative model.
arXiv Detail & Related papers (2023-02-20T00:53:33Z) - Improving Adversarial Robustness by Contrastive Guided Diffusion Process [19.972628281993487]
We propose Contrastive-Guided Diffusion Process (Contrastive-DP) to guide the diffusion model in data generation.
We show that enhancing the distinguishability among the generated data is critical for improving adversarial robustness.
arXiv Detail & Related papers (2022-10-18T07:20:53Z) - Improving Model Compatibility of Generative Adversarial Networks by
Boundary Calibration [24.28407308818025]
Boundary-Calibration GANs (BCGANs) are proposed to improve GAN's model compatibility.
BCGANs generate realistic images like original GANs but also achieves superior model compatibility than the original GANs.
arXiv Detail & Related papers (2021-11-03T16:08:09Z) - Negative Data Augmentation [127.28042046152954]
We show that negative data augmentation samples provide information on the support of the data distribution.
We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator.
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
arXiv Detail & Related papers (2021-02-09T20:28:35Z)
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.