The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Systematic evaluation of convolution neural network advances on the Imagenet
Open accessAbstract
Abstract The paper systematically studies the impact of a range of recent advances in convolution neural network (CNN) architectures and learning methods on the object categorization (ILSVRC) problem. The evaluation tests the influence of the following choices of the architecture: non-linearity (ReLU, ELU, maxout, compatability with batch normalization), pooling variants (stochastic, max, average, mixed), network width, classifier design (convolutional, fully-connected, SPP), image pre-processing, and of learning parameters: learning rate, batch size, cleanliness of the data, etc. The performance gains of the proposed modifications are first tested individually and then in combination. The sum of individual gains is greater than the observed improvement when all modifications are introduced, but the “deficit” is small suggesting independence of their benefits. We show that the use of 128 × 128 pixel images is sufficient to make qualitative conclusions about optimal network structure that hold for the full size Caffe and VGG nets. The results are obtained an order of magnitude faster than with the standard 224 pixel images.
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ImageNet Large Scale Visual Recognition Challenge
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Ensemble Adversarial Training: Attacks and Defenses
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Opportunities for neuromorphic computing algorithms and applications
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Metadata
- Authors
- Mishkin, D., Sergievskiy, N., & Matas, J.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 161
- Pages
- 11-19
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2418033038
- Cited by
- 308
- References
- 63
- FWCI
- 16.40
- Type
- journal article
- Language
- en
- Open access
- green
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