Simple Fast Convolutional Feature Learning

David Macêdo, Cleber Zanchettin, Teresa Ludermir

Feb 15, 2018 (modified: Oct 26, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: The quality of the features used in visual recognition is of fundamental importance for the overall system. For a long time, low-level hand-designed feature algorithms as SIFT and HOG have obtained the best results on image recognition. Visual features have recently been extracted from trained convolutional neural networks. Despite the high-quality results, one of the main drawbacks of this approach, when compared with hand-designed features, is the training time required during the learning process. In this paper, we propose a simple and fast way to train supervised convolutional models to feature extraction while still maintaining its high-quality. This methodology is evaluated on different datasets and compared with state-of-the-art approaches.
  • TL;DR: A simple fast method for extracting visual features from convolutional neural networks
  • Keywords: Feature Learning, Convolutional Neural Networks, Visual Recognition
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