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In our work, we propose a novel yet simple approach to obtain an adaptivelearning rate for gradient-based descent methods on classification tasks.Instead of the traditional approach of selecting adaptive learning rates viathe decayed expectation of gradient-based terms, we use the angle between thecurrent gradient and the new gradient: this new gradient is computed from thedirection orthogonal to the current gradient, which further helps us indetermining a better adaptive learning rate based on angle history, thereby,leading to relatively better accuracy compared to the existing state-of-the-artoptimizers. On a wide variety of benchmark datasets with prominent imageclassification architectures such as ResNet, DenseNet, EfficientNet, and VGG,we find that our method leads to the highest accuracy in most of the datasets.Moreover, we prove that our method is convergent.

Angle based dynamic learning rate for gradient descent