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We propose a learning rate adaptation scheme, called QLAB, for descentoptimizers. We derive QLAB by optimizing the quadratic approximation of theloss function and QLAB can be combined with any optimizer who can provide thedescent update direction. The computation of an adaptive learning rate withQLAB requires only computing an extra loss function value. We theoreticallyprove the convergence of the descent optimizers with QLAB. We demonstrate theeffectiveness of QLAB in a range of optimization problems by combining withconclusively stochastic gradient descent, stochastic gradient descent withmomentum, and Adam. The performance is validated on multi-layer neuralnetworks, CNN, VGG-Net, ResNet and ShuffleNet with two datasets, MNIST andCIFAR10.

QLAB: Quadratic Loss Approximation-Based Optimal Learning Rate for Deep Learning