Abstract: A computer-implemented method for improving generalization in training deep neural networks in online settings. The method includes a general learning paradigm for sequential data that is referred to as Learn, Unlearn, RElearn (LURE), a dynamic re-initialization method to address the above-mentioned larger problem of generalization of parameterized networks on sequential data by selectively retaining the task-specific connections through the important criteria and re-randomizing the less important parameters at each mega batch of training. The method of selectively forgetting retains previous information all the while improving generalization to unseen samples.

Abstract: A computer implemented network for executing a self-supervised scene change detection method in which image pairs (T0, T1) from different time instances are subjected to random photometric transformations to obtain two pairs of augmented images (T0 ? T 0?, T 0? ; T1 ? T 1?, T1?), which augmented images are passed into an encoder (f?) and a projection head (g?) to provide corresponding feature representations.

Abstract: A computer implemented network for executing a self-supervised scene change detection method, wherein at least one image pair with images captured at different instances of time is processed to detect structural changes caused by an appearance or disappearance of an object in the image pair, and wherein a self-supervised pretraining method is employed that utilizes an unlabelled image pair or pairs to learn representations for scene change detection, and wherein the aligned image pair is subjected to a differencing based self-supervised pre-training method to maximize a correlation between changed regions in the images which provide the structural changes that occur in the image pairs.