QLAB: Quadratic Loss Approximation-Based Optimal Learning Rate for Deep Learning
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We propose a learning rate adaptation scheme, called QLAB, for descent optimizers. We derive QLAB by optimizing the quadratic approximation of the loss function and QLAB can be combined with any optimizer who can provide the descent update direction. The computation of an adaptive learning rate with QLAB requires only computing an extra loss function value. We theoretically prove the convergence of the descent optimizers with QLAB. We demonstrate the effectiveness of QLAB in a range of optimization problems by combining with conclusively stochastic gradient descent, stochastic gradient descent with momentum, and Adam. The performance is validated on multi-layer neural networks, CNN, VGG-Net, ResNet and ShuffleNet with two datasets, MNIST and CIFAR10.
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