  The success of deep learning can be attributed to various factors such as
increase in computational power, large datasets, deep convolutional neural
networks, optimizers etc. Particularly, the choice of optimizer affects the
generalization, convergence rate, and training stability. Stochastic Gradient
Descent (SGD) is a first order iterative optimizer that updates the gradient
uniformly for all parameters. This uniform update may not be suitable across
the entire training phase. A rudimentary solution for this is to employ a
fine-tuned learning rate scheduler which decreases learning rate as a function
of iteration. To eliminate the dependency of learning rate schedulers, adaptive
gradient optimizers such as AdaGrad, AdaDelta, RMSProp, Adam employ a
parameter-wise scaling term for learning rate which is a function of the
gradient itself. We propose Tom (Trend over Momentum) optimizer, which is a
novel variant of Adam that takes into account of the trend which is observed
for the gradients in the loss landscape traversed by the neural network. In the
proposed Tom optimizer, an additional smoothing equation is introduced to
address the trend observed during the process of optimization. The smoothing
parameter introduced for the trend requires no tuning and can be used with
default values. Experimental results for classification datasets such as
CIFAR-10, CIFAR-100 and CINIC-10 image datasets show that Tom outperforms
Adagrad, Adadelta, RMSProp and Adam in terms of both accuracy and has a faster
convergence. The source code is publicly made available at
https://github.com/AnirudhMaiya/Tom
