An Out-of-the-box Full-network Embedding for Convolutional Neural Networks

ICLR 2018 Dario Garcia-GasullaArmand VilaltaFerran ParésJonatan MorenoEduard AyguadéJesus LabartaUlises CortésToyotaro Suzumura

Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option. While previous contributions to feature extraction propose embeddings based on a single layer of the network, in this paper we propose a full-network embedding which successfully integrates convolutional and fully connected features, coming from all layers of a deep convolutional neural network... (read more)

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