Aspects of the present disclosure describe techniques for training a convolutional neural network using an inconsistent stochastic gradient descent (ISGD) algorithm. Training effort for training batches used by the ISGD algorithm are dynamically adjusted according to a determined loss for a given training batch which are classified into two sub states—well-trained or under-trained. The ISGD algorithm provides more iterations for under-trained batches while reducing iterations for well-trained ones.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: training, by at least one computing device, a convolutional neural network (CNN) with an inconsistent stochastic gradient descent (ISGD) algorithm using a training data set; wherein said training includes executing the ISGD algorithm for a number of iterations 2. The method of claim 1 wherein said computing device includes a plurality of processors and said method further comprises parallelizing, by the at least one computing device, at least a portion of any computations of the inconsistent stochastic gradient descent algorithm on the plurality of processors. 3. The method of claim 2 further comprising training the CNN with batches of training data, and classifying the batches as under-trained status or well-trained status based upon a loss determination. 4. The method of claim 3 further comprising continuing to train any batches classified as under-trained until its determined loss falls below a pre-determined threshold or a pre-determined number of iterations is reached. 5. The method of claim 3 further comprising rebalancing training effort across batches with respect to a given batch's training status. 6. The method of claim 4 wherein said continued training is defined by the following problem: min w  φ w  ( d t ) = 1 2  P   ψ w  ( d t ) - limit   P 2 2 + ɛ 2  n w  Pw - w t - 1  P 2 2 where nw is the number of weight parameters in the network and ε is a parameter for the second term, and the first term minimizes the difference between the loss of current under-trained batch dt and a control limit. 7. The method of claim 3 wherein a batch is classified as under-trained if the loss is larger than a control limit defined by: ψ+3*σψ.