ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection

1 code implementation • 26 May 2023 • Jeeho Hyun, Sangyun Kim, Giyoung Jeon, Seung Hwan Kim, Kyunghoon Bae, Byung Jun Kang

In this paper, we introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation of patch features extracted from the pre-trained model.

Anomaly Detection Contrastive Learning +2

Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling

no code implementations • ICCV 2023 • Giyoung Jeon, Haedong Jeong, Jaesik Choi

We show that such noisy attribution can be reduced by aggregating attributions from the multiple paths instead of using a single path.

On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network

no code implementations • 7 Jul 2022 • SeongJin Park, Haedong Jeong, Giyoung Jeon, Jaesik Choi

In general, Deep Neural Networks (DNNs) are evaluated by the generalization performance measured on unseen data excluded from the training phase.

Adversarial Attack Adversarial Robustness

Empirical Study of the Decision Region and Robustness in Deep Neural Networks

no code implementations • 29 Sep 2021 • SeongJin Park, Haedong Jeong, Giyoung Jeon, Jaesik Choi

In general, the Deep Neural Networks (DNNs) is evaluated by the generalization performance measured on the unseen data excluded from the training phase.

Adversarial Attack Adversarial Robustness

An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks

no code implementations • 12 Dec 2019 • Giyoung Jeon, Haedong Jeong, Jaesik Choi

Despite of recent advances in generative networks, identifying the image generation mechanism still remains challenging.

Image Generation