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This study investigates imposing hard inequality constraints on the outputs of convolutional neural networks (CNN) during training.
Convex Optimization
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Imposing Hard Constraints on Deep Networks: Promises and Limitations
Pablo Márquez-Neila, Mathieu Salzmann, and Pascal Fua · 2017
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Deep generative models with learnable knowledge constraints
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Constrained-cnn losses for weakly supervised segmentation
Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, and Ismail Ben Ayed · 2019
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A primal dual formulation for deep learning with constraints
Yatin Nandwani, Abhishek Pathak, Parag Singla, et al · 2019
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Explicitly imposing constraints in deep networks via conditional gradients gives improved generalization and faster convergence
Sathya N Ravi, Tuan Dinh, Vishnu Suresh Lokhande, and Vikas Singh · 2019
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Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
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A curriculum domain adaptation approach to the semantic segmentation of urban scenes
Yang Zhang, Philip David, Hassan Foroosh, and Boqing Gong · 2019
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Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, and Yung-Yu Chuang · 2019
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Prior-aware neural network for partially-supervised multi-organ segmentation
Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, and Alan Yuille · 2019
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