ImageNet classification with deep convolutional neural networks¶
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (NIPS 2012), 25, 1097-1105.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Lateral Inhibition
- Machine learning: Winner-take-all layers and local-response-normalization schemes let strongly activated units suppress nearby units, enforcing sparse, decorrelated representations in which only the most salient features survive.
This sourceIntroduces local response normalization, in which a strongly activated unit suppresses neighboring units' normalized responses—a lateral-inhibition mechanism enforcing sparse, decorrelated, winner-take-all feature representations.
- Machine learning: Winner-take-all layers and local-response-normalization schemes let strongly activated units suppress nearby units, enforcing sparse, decorrelated representations in which only the most salient features survive.
- Pattern Recognition
- The tension arises because the two can be conflated: computer vision systems that perform template-matching are sometimes mistaken for perception-like recognition; conversely, human perception is sometimes treated as if it were literal matching.
This sourceIntroduces local response normalization, in which a strongly activated unit suppresses neighboring units' normalized responses—a lateral-inhibition mechanism enforcing sparse, decorrelated, winner-take-all feature representations.
- The tension arises because the two can be conflated: computer vision systems that perform template-matching are sometimes mistaken for perception-like recognition; conversely, human perception is sometimes treated as if it were literal matching.
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