Deep Epitomic Convolutional Neural Networks
By George Papandreou. 2014
7 citations recorded by the source corpus.
Deep convolutional neural networks have recently proven extremely competitive in challenging image recognition tasks. This paper proposes the epitomic convolution as a new building block for deep neural networks. An epitomic convolution layer replaces a pair of consecutive convolution and max-pooling layers found in standard deep convolutional neural networks. The main version of the proposed model uses mini-epitomes in place of filters and computes responses invariant to small translations by epitomic search instead of max-pooling over image positions. The topographic version of the proposed model uses large epitomes to learn filter maps organized in translational topographies. We show that error back-propagation can successfully learn multiple epitomic layers in a supervised fashion. The effectiveness of the proposed method is assessed in image classification tasks on standard benchmarks. Our experiments on Imagenet indicate improved recognition performance compared to standard convolutional neural networks of similar architecture. Our models pre-trained on Imagenet perform excellently on Caltech-101. We also obtain competitive image classification results on the small-image MNIST and CIFAR-10 datasets.
Subfields: Computer Vision and Pattern Recognition, Model Architectures
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Alan L. Yuille
- Dacheng Tao
- Xiaogang Wang
- Li Fei-Fei
- Andrea Vedaldi
- Andrew Zisserman
In Model Architectures
- Iasonas Kokkinos
- George Papandreou
- Pierre-André Savalle
- Li Fei-Fei
- Hao Su
- Sanjeev Satheesh
- Michael S. Bernstein
- Jia Deng
Citations in the corpus
- ImageNet Large Scale Visual Recognition Challenge (2014, background): “3 - - Toyota Technological Institute at Chicago†, Ecole Centrale Paris‡ George Papandreou†, Iasonas Kokkinos‡(Papandreou, 2014; Papandreou et al., 2014; Jojic et al., 2003; Krizhevsky et al., 2012; Sermanet et al., 2013; Dubout and Fleuret, 2012; Iandola et al., 2014)”
- Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection (2015, methodology): “In [34] we report further successful experiments with deep epitomic networks on the Caltech-101, MNIST, and CIFAR-10 datasets.”