Data augmentation is a widely used technique in many machine learning tasks, such as image classification, to virtually enlarge the training dataset size and avoid overfitting. Traditional data augmentation techniques for image classification tasks create new samples from the original training data by, for example, flipping, distorting, adding a small amount of noise to, or cropping a patch fro...

Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) restoration, enabling represent implicit using only convolutional neural network architecture without training dataset, whereas the general supervised approach requires massive low-and high-quality PET pairs. To answer increased need for imaging with DIP, it is indispensable improve performance of underly...

Gender classification is a binary classification system where system has to assign a given test image to one of the two classes (male or female). The gender classification system with large set of training data normally gives good accuracy. But to achieve good accuracy with small training data is a difficult task. This paper proposes an algorithm for gender classification with small training da...

Recent work has shown that self-supervised pre-training leads to improvements over supervised learning on challenging visual recognition tasks. CLIP, an exciting new approach with language supervision, demonstrates promising performance a wide variety of benchmarks. In this work, we explore whether can aid in the use supervision for representation Vision Transformers. We introduce SLIP, multi-t...