Fine-tuning with sign-symmetry rules preserves accuracy while resisting white-box adversarial attacks, but the robustness appears to be gradient masking rather than genuine defense.
Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval
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abstract
Deep hashing techniques have emerged as the predominant approach for efficient image retrieval. Traditionally, these methods utilize pre-trained convolutional neural networks (CNNs) such as AlexNet and VGG-16 as feature extractors. However, the increasing complexity of datasets poses challenges for these backbone architectures in capturing meaningful features essential for effective image retrieval. In this study, we explore the efficacy of employing high-resolution features learned through state-of-the-art techniques for image retrieval tasks. Specifically, we propose a novel methodology that utilizes High-Resolution Networks (HRNets) as the backbone for the deep hashing task, termed High-Resolution Hashing Network (HHNet). Our approach demonstrates superior performance compared to existing methods across all tested benchmark datasets, including CIFAR-10, NUS-WIDE, MS COCO, and ImageNet. This performance improvement is more pronounced for complex datasets, which highlights the need to learn high-resolution features for intricate image retrieval tasks. Furthermore, we conduct a comprehensive analysis of different HRNet configurations and provide insights into the optimal architecture for the deep hashing task
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cs.LG 1years
2025 1verdicts
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Sign-Symmetry Learning Rules are Robust Fine-Tuners
Fine-tuning with sign-symmetry rules preserves accuracy while resisting white-box adversarial attacks, but the robustness appears to be gradient masking rather than genuine defense.