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In the rapidly advancing domain of deep learning optimization, this paperunveils the StochGradAdam optimizer, a novel adaptation of the well-regardedAdam algorithm. Central to StochGradAdam is its gradient sampling technique.This method not only ensures stable convergence but also leverages theadvantages of selective gradient consideration, fostering robust training bypotentially mitigating the effects of noisy or outlier data and enhancing theexploration of the loss landscape for more dependable convergence. In bothimage classification and segmentation tasks, StochGradAdam has demonstratedsuperior performance compared to the traditional Adam optimizer. By judiciouslysampling a subset of gradients at each iteration, the optimizer is optimizedfor managing intricate models. The paper provides a comprehensive explorationof StochGradAdam's methodology, from its mathematical foundations to biascorrection strategies, heralding a promising advancement in deep learningtraining techniques.

StochGradAdam: Accelerating Neural Networks Training with Stochastic Gradient Sampling