4,715 research outputs found
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
Global covariance pooling in convolutional neural networks has achieved
impressive improvement over the classical first-order pooling. Recent works
have shown matrix square root normalization plays a central role in achieving
state-of-the-art performance. However, existing methods depend heavily on
eigendecomposition (EIG) or singular value decomposition (SVD), suffering from
inefficient training due to limited support of EIG and SVD on GPU. Towards
addressing this problem, we propose an iterative matrix square root
normalization method for fast end-to-end training of global covariance pooling
networks. At the core of our method is a meta-layer designed with loop-embedded
directed graph structure. The meta-layer consists of three consecutive
nonlinear structured layers, which perform pre-normalization, coupled matrix
iteration and post-compensation, respectively. Our method is much faster than
EIG or SVD based ones, since it involves only matrix multiplications, suitable
for parallel implementation on GPU. Moreover, the proposed network with ResNet
architecture can converge in much less epochs, further accelerating network
training. On large-scale ImageNet, we achieve competitive performance superior
to existing counterparts. By finetuning our models pre-trained on ImageNet, we
establish state-of-the-art results on three challenging fine-grained
benchmarks. The source code and network models will be available at
http://www.peihuali.org/iSQRT-COVComment: Accepted to CVPR 201
Fast ConvNets Using Group-wise Brain Damage
We revisit the idea of brain damage, i.e. the pruning of the coefficients of
a neural network, and suggest how brain damage can be modified and used to
speedup convolutional layers. The approach uses the fact that many efficient
implementations reduce generalized convolutions to matrix multiplications. The
suggested brain damage process prunes the convolutional kernel tensor in a
group-wise fashion by adding group-sparsity regularization to the standard
training process. After such group-wise pruning, convolutions can be reduced to
multiplications of thinned dense matrices, which leads to speedup. In the
comparison on AlexNet, the method achieves very competitive performance
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