Imagenet classification with deep convolutional neural networks Imagenet classification with deep convolutional neural networks
Paper summary This paper introduces a deep convolutional neural network (CNN) architecture that achieved record-breaking performance in the 2012 ImageNet LSVRC. Notably, it brings together a bunch of neat ideas in an end-to-end, trainable model. Main contributions: - Achieves state-of-the-art performance in ILSVRC-2012. - Makes available an efficient, parallelized GPU implementation of their model. - Describes in detail the features of their model that help in improving performance and reducing training time, along with extensive ablative studies. - Uses data augmentation and dropout to prevent overfitting. ## Strengths - Uses (and popularizes) ReLUs instead of tanh as the non-linear activation unit, which makes training six times faster. - Uses local response normalization and overlapped pooling. - Data augmentation - Extracts random crops and performs image translations, horizontal reflections maintaining the label distribution. - Alters RGB pixel values by performing PCA on training set, and adding multiples of eigenvalues times a random variable drawn from a Gaussian to image. Provides invariance to changes in intensity and color of illumination. - Dropout prevents overfitting. Randomly drops half of the neurons in the fully connected layers, and can be interpreted as averaging over exponentially-many dropout networks. ## Weaknesses / Notes - Lacks theoretical insight. Design decisions are motivated solely by results.

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