Search Results for author:
Found 7 papers, 5 papers with code
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation
Scarcity of labeled data has motivated the development of semi-supervised learning methods, which learn from large portions of unlabeled data alongside a few labeled samples.
Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping
Many image enhancement or editing operations, such as forward and inverse tone mapping or color grading, do not have a unique solution, but instead a range of solutions, each representing a different style.
Training a Task-Specific Image Reconstruction Loss
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution.
Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation
Scarcity of labeled data has motivated the development of semi-supervised learning methods, which learn from large portions of unlabeled data alongside a few labeled samples.
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images.
Ranked #7 on
Adversarial Defense
on CIFAR-10
Image Super-Resolution as a Defense Against Adversarial Attacks
The proposed scheme is simple and has the following advantages: (1) it does not require any model training or parameter optimization, (2) it complements other existing defense mechanisms, (3) it is agnostic to the attacked model and attack type and (4) it provides superior performance across all popular attack algorithms.
Prediction and Localization of Student Engagement in the Wild
Recognizing the lack of any publicly available dataset in the domain of user engagement, a new `in the wild' dataset is created to study the subject engagement problem.
