Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
This addresses a computational bottleneck for researchers and practitioners using covariance pooling in deep learning, offering faster training times, though it is incremental as it builds on existing normalization techniques.
The paper tackles the slow training of global covariance pooling networks by proposing an iterative matrix square root normalization method that replaces inefficient eigendecomposition or singular value decomposition with faster matrix multiplications, achieving competitive performance on ImageNet and state-of-the-art results on fine-grained benchmarks.
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-COV