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In many machine learning applications, labeled data is scarce and obtaining more labels is expensive.
- We introduce a new approach to supervising neural networks by specifying constraints that should hold over the output space, rather than direct examples of input-output pairs.
- These constraints are derived from prior domain knowledge, e.g., from known laws of physics.
- We demonstrate the effectiveness of this approach on real world and simulated computer vision tasks.
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Learning compact binary descriptors with unsupervised deep neural networks
Lin, K.; Lu, J.; Chen, C.-S.; and Zhou, J
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