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Jiamian Wang, Yulun Zhang, Xin Yuan, Ziyi Meng, Zhiqiang Tao

Recently, hyperspectral imaging (HSI) has attracted increasing research attention, especially for the ones based on a coded aperture snapshot spectral imaging (CASSI) system. Existing deep HSI reconstruction models are generally trained on paired data to retrieve original signals upon 2D compressed measurements given by a particular optical hardware mask in CASSI, during which the mask largely impacts the reconstruction performance and could work as a ""model hyperparameter"" governing on data augmentations. This mask-specific training style will lead to a hardware miscalibration issue, which sets up barriers to deploying deep HSI models among different hardware and noisy environments. To address this challenge, we introduce mask uncertainty for HSI with a complete variational Bayesian learning treatment and explicitly model it through a mask decomposition inspired by real hardware. Specifically, we propose a novel Graph-based Self-Tuning (GST) network to reason uncertainties adapting to varying spatial structures of masks among different hardware. Moreover, we develop a bilevel optimization framework to balance HSI reconstruction and uncertainty estimation, accounting for the hyperparameter property of masks. Extensive experimental results validate the effectiveness (over 33/30 dB) of the proposed method under two miscalibration scenarios and demonstrate a highly competitive performance compared with the state-of-the-art well-calibrated methods. Our source code and pre-trained models are available at https://github.com/Jiamian-Wang/mask_uncertainty_spectral_SCI

Yaguan Qian, Shenghui Huang, Bin Wang, Xiang Ling, Xiaohui Guan, Zhaoquan Gu, Shaoning Zeng, Wujie Zhou, Haijiang Wang

The vulnerability of DNNs severely limits the application of it in the security-sensitive domains. Most of the existing methods improve the robustness of models from weight optimization, such as adversarial training and regularization. However, the architecture is also a key factor to robustness, which is often neglected or underestimated. We propose Robust Network Architecture Search (RNAS) to obtain a robust network against adversarial attacks. We observe that an adversarial perturbation distorting the non-robust features in latent feature space can further aggravate misclassification. Based on this observation, we search the robust architecture through restricting feature distortion in the search process. Specifically, we define a network vulnerability metric based on feature distortion as a constraint in the search process. This process is modeled as a multi-objective bilevel optimization problem and an effective algorithm is proposed to solve this optimization. Extensive experiments conducted on CIFAR-10/100, SVHN and Tiny-ImageNet show that RNAS achieves the best robustness under various adversarial attacks compared with extensive baselines and state-of-the-art methods.