Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection

• URL: http://arxiv.org/abs/2406.00501v1
• Date: Sat, 1 Jun 2024 17:09:18 GMT
• Title: Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection
• Authors: Luigi Capogrosso, Federico Girella, Francesco Taioli, Michele Dalla Chiara, Muhammad Aqeel, Franco Fummi, Francesco Setti, Marco Cristani,
• Abstract summary: We show that diffusion models can be used in industrial scenarios to improve the data augmentation procedure. We propose a novel approach for data augmentation that mixes out-of-distribution with in-distribution samples.
• Score: 8.93281936150572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we show that diffusion models can be used in industrial scenarios to improve the data augmentation procedure in the context of surface defect detection. In general, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are consistently fewer than normal samples. For these reasons, state-of-the-art data augmentation procedures add synthetic defect data by superimposing artifacts to normal samples. This leads to out-of-distribution augmented data so that the classification system learns what is not a normal sample but does not know what a defect really is. We show that diffusion models overcome this situation, providing more realistic in-distribution defects so that the model can learn the defect's genuine appearance. We propose a novel approach for data augmentation that mixes out-of-distribution with in-distribution samples, which we call In&Out. The approach can deal with two data augmentation setups: i) when no defects are available (zero-shot data augmentation) and ii) when defects are available, which can be in a small number (few-shot) or a large one (full-shot). We focus the experimental part on the most challenging benchmark in the state-of-the-art, i.e., the Kolektor Surface-Defect Dataset 2, defining the new state-of-the-art classification AP score under weak supervision of .782. The code is available at https://github.com/intelligolabs/in_and_out.

Related papers

• Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection [9.784793380119806]
  We introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation. Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model. We demonstrate the efficacy and versatility of DIAG with respect to state-of-the-art data augmentation approaches on the challenging KSDD2 dataset.
  arXiv  Detail & Related papers  (2024-07-04T14:28:52Z)
• GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [60.78684630040313]
  Diffusion models tend to reconstruct normal counterparts of test images with certain noises added. From the global perspective, the difficulty of reconstructing images with different anomalies is uneven. We propose a global and local adaptive diffusion model (abbreviated to GLAD) for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2024-06-11T17:27:23Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models [72.07462371883501]
  We propose emphProjection Regret (PR), an efficient novelty detection method that mitigates the bias of non-semantic information. PR computes the perceptual distance between the test image and its diffusion-based projection to detect abnormality. Extensive experiments demonstrate that PR outperforms the prior art of generative-model-based novelty detection methods by a significant margin.
  arXiv  Detail & Related papers  (2023-12-05T09:44:47Z)
• RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision [21.393509817509464]
  This paper proposes a novel semi-supervised anomaly detection method, which devises textitcontamination-resilient continuous supervisory signals Our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR.
  arXiv  Detail & Related papers  (2023-07-25T04:04:49Z)
• TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models [5.314466196448187]
  We present a diffusion-based probabilistic model effective for unsupervised anomaly detection. Our model is trained to learn the density of normal samples by utilizing a unique rejection scheme. At inference, we identify anomalies as samples in low-density regions.
  arXiv  Detail & Related papers  (2023-07-23T14:02:33Z)
• Augment to Detect Anomalies with Continuous Labelling [10.646747658653785]
  Anomaly detection is to recognize samples that differ in some respect from the training observations. Recent state-of-the-art deep learning-based anomaly detection methods suffer from high computational cost, complexity, unstable training procedures, and non-trivial implementation. We leverage a simple learning procedure that trains a lightweight convolutional neural network, reaching state-of-the-art performance in anomaly detection.
  arXiv  Detail & Related papers  (2022-07-03T20:11:51Z)
• Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models [54.182955830194445]
  existing models either fail or face a dramatic drop under the so-called near-distribution" setting. We propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data. Our method improves the near-distribution novelty detection by 6% and passes the state-of-the-art by 1% to 5% across nine novelty detection benchmarks.
  arXiv  Detail & Related papers  (2022-05-28T02:02:53Z)
• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation [77.71161225100927]
  Anomaly detection is a fundamental yet challenging problem in machine learning. We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-11-25T03:53:43Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)
• Deep Visual Anomaly detection with Negative Learning [18.79849041106952]
  In this paper, we propose anomaly detection with negative learning (ADNL), which employs the negative learning concept for the enhancement of anomaly detection. The idea is to limit the reconstruction capability of a generative model using the given a small amount of anomaly examples. This way, the network not only learns to reconstruct normal data but also encloses the normal distribution far from the possible distribution of anomalies.
  arXiv  Detail & Related papers  (2021-05-24T01:48:44Z)

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.