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Visual Learning and Recognition: Difference between revisions
Visual Learning and Recognition (view source)
Revision as of 16:41, 24 September 2020
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==ConvNets and Architectures== | ==ConvNets and Architectures== | ||
See [[Convolutional neural network]] for basics. | |||
===Paper Summaries=== | ===Paper Summaries=== | ||
Krizhevsky ''et al.''<ref name="krizhevsky2012alexnet"></ref> develop AlexNet for image classification. AlexNet is a CNN architecture with two branches. Their architecture and training proceedure includes many tricks, some of which are now commonplace today. These include multi-GPU training, 8 layers, ReLU activations, Local Response Normalization (LRU), (overlapping) max pooling, data augmentation, and dropout. They won on ImageNet 2012 by a large margin. | Krizhevsky ''et al.''<ref name="krizhevsky2012alexnet"></ref> develop AlexNet for image classification. AlexNet is a CNN architecture with two branches. Their architecture and training proceedure includes many tricks, some of which are now commonplace today. These include multi-GPU training, 8 layers, ReLU activations, Local Response Normalization (LRU), (overlapping) max pooling, data augmentation, and dropout. They won on ImageNet 2012 by a large margin. | ||