We propose a self-supervised learning methos that utilizes sparsity and dictionary learning-based approach for single-image denoising where the noise is approximated as a Poisson process, requiring no clean ground-truth data.
We propose Ada-VSR framework that employs meta-learning to obtain adaptive model parameters, using a large-scale external dataset, that can adapt quickly to novel conditions of the given test video, thereby exploiting external and internal information of a video for super-resolution.
We introduce a novel architecture, Adaptive Latent Attention Network (ALANET), which synthesizes sharp high frame-rate videos with no prior knowledge of input frames being blurry or not, thereby performing the task of both deblurring and interpolation.
We present a novel adversarial framework for transferring knowledge from internet-scale unlabeled data to improve the performance of a classifier on a given visual recognition task.
We present a non-adversarial approach for video synthesis by jointly optimizing the latent space and the model parametres.
We propose a data-driven Deep Quantized Latent Representation (DQLR) methodology for high-quality image reconstruction in the Shoot Apical Meristem (SAM) of Arabidopsis thaliana by utilizing multiple consecutive slices in the z-stack
We present a Siamese Convolutional Neural Networks (CNN) based deep learning algorithm for the task of face recognition in renaissance portraits.