Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training

• URL: http://arxiv.org/abs/2411.02149v2
• Date: Tue, 05 Nov 2024 03:41:28 GMT
• Title: Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training
• Authors: Yuanqi Yao, Gang Wu, Kui Jiang, Siao Liu, Jian Kuai, Xianming Liu, Junjun Jiang,
• Abstract summary: We propose a general adversarial training framework, named Stabilized Conflict-optimization Adversarial Training (SCAT) SCAT integrates adversarial data augmentation into self-supervised MDE methods to achieve a balance between stability and generalization. Experiments on five benchmarks demonstrate that SCAT can achieve state-of-the-art performance and significantly improve the generalization capability of existing self-supervised MDE methods.
• Score: 61.35809887986553
• License:
• Abstract: Learning a self-supervised Monocular Depth Estimation (MDE) model with great generalization remains significantly challenging. Despite the success of adversarial augmentation in the supervised learning generalization, naively incorporating it into self-supervised MDE models potentially causes over-regularization, suffering from severe performance degradation. In this paper, we conduct qualitative analysis and illuminate the main causes: (i) inherent sensitivity in the UNet-alike depth network and (ii) dual optimization conflict caused by over-regularization. To tackle these issues, we propose a general adversarial training framework, named Stabilized Conflict-optimization Adversarial Training (SCAT), integrating adversarial data augmentation into self-supervised MDE methods to achieve a balance between stability and generalization. Specifically, we devise an effective scaling depth network that tunes the coefficients of long skip connection and effectively stabilizes the training process. Then, we propose a conflict gradient surgery strategy, which progressively integrates the adversarial gradient and optimizes the model toward a conflict-free direction. Extensive experiments on five benchmarks demonstrate that SCAT can achieve state-of-the-art performance and significantly improve the generalization capability of existing self-supervised MDE methods.

Related papers

• Conflict-Aware Adversarial Training [29.804312958830636]
  We argue that the weighted-average method does not provide the best tradeoff for the standard performance and adversarial robustness. We propose a new trade-off paradigm for adversarial training with a conflict-aware factor for the convex combination of standard and adversarial loss, named textbfConflict-Aware Adrial Training(CA-AT)
  arXiv  Detail & Related papers  (2024-10-21T23:44:03Z)
• Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks [36.16206095819624]
  Monocular Depth Estimation plays a vital role in applications such as autonomous driving. Traditional adversarial training methods, which require ground-truth labels, are not directly applicable to MDE models that lack ground-truth depth. We introduce a novel self-supervised adversarial training approach for MDE models, leveraging view synthesis without the need for ground-truth depth.
  arXiv  Detail & Related papers  (2024-06-09T17:02:28Z)
• The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks [90.52808174102157]
  In safety-critical applications such as medical imaging and autonomous driving, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks. A notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. This study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks.
  arXiv  Detail & Related papers  (2024-05-14T18:05:19Z)
• Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification [9.039586043401972]
  adversarial examples (AEs) exhibited imperceptible similarity with raw data, raising serious concerns on network robustness. In this paper, we propose a dynamic perturbation-adaptive adversarial training (DPAAT) method, which placed AT in a dynamic learning environment to generate adaptive data-level perturbations. Comprehensive testing on dermatology HAM10000 dataset showed that the DPAAT not only achieved better robustness improvement and generalization preservation but also significantly enhanced mean average precision and interpretability.
  arXiv  Detail & Related papers  (2024-03-11T15:16:20Z)
• Building Robust Ensembles via Margin Boosting [98.56381714748096]
  In adversarial robustness, a single model does not usually have enough power to defend against all possible adversarial attacks. We develop an algorithm for learning an ensemble with maximum margin. We show that our algorithm not only outperforms existing ensembling techniques, but also large models trained in an end-to-end fashion.
  arXiv  Detail & Related papers  (2022-06-07T14:55:58Z)
• Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization [9.686724616328874]
  Adversarial training (AT) has proven to be one of the most effective ways to defend Deep Neural Networks (DNNs) against adversarial attacks. robustness will drop sharply at a certain stage, always exists during AT. consistency regularization, a popular technique in semi-supervised learning, has a similar goal as AT and can be used to alleviate robust overfitting.
  arXiv  Detail & Related papers  (2022-05-24T03:18:43Z)
• Enhancing Adversarial Training with Feature Separability [52.39305978984573]
  We introduce a new concept of adversarial training graph (ATG) with which the proposed adversarial training with feature separability (ATFS) enables to boost the intra-class feature similarity and increase inter-class feature variance. Through comprehensive experiments, we demonstrate that the proposed ATFS framework significantly improves both clean and robust performance.
  arXiv  Detail & Related papers  (2022-05-02T04:04:23Z)
• On the Convergence and Robustness of Adversarial Training [134.25999006326916]
  Adrial training with Project Gradient Decent (PGD) is amongst the most effective. We propose a textitdynamic training strategy to increase the convergence quality of the generated adversarial examples. Our theoretical and empirical results show the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2021-12-15T17:54:08Z)
• On the Generalization Properties of Adversarial Training [21.79888306754263]
  This paper studies the generalization performance of a generic adversarial training algorithm. A series of numerical studies are conducted to demonstrate how the smoothness and L1 penalization help improve the adversarial robustness of models.
  arXiv  Detail & Related papers  (2020-08-15T02:32:09Z)
• Efficient Empowerment Estimation for Unsupervised Stabilization [75.32013242448151]
  empowerment principle enables unsupervised stabilization of dynamical systems at upright positions. We propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel. We show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images.
  arXiv  Detail & Related papers  (2020-07-14T21:10:16Z)

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.