In this paper, we propose a learning-based supervised discrete hashing (SDH) method.
Binary hashing is widely used for large-scale image retrieval as well as video and document …

Abstract This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-
labeling algorithm for unsupervised representation learning inspired by information …

Most self-supervised methods for representation learning leverage a cross-view consistency
objective ie, they maximize the representation similarity of a given image's augmented …

Contrastive loss has significantly improved performance in supervised classification tasks by
using a multi-viewed framework that leverages augmentation and label information. The …

Deep neural networks are known to be vulnerable to security risks due to the inherent
transferable nature of adversarial examples. Despite the success of recent generative model …

Self-supervised learning aims to learn representation that can be effectively generalized to
downstream tasks. Many self-supervised approaches regard two views of an image as both …

We propose a novel adaptive transfer learning framework, learning to transfer learn (L2TL),
to improve performance on a target dataset by careful extraction of the related information …

In recent years, vehicle classification has been one of the most important research topics.
However, due to the lack of a proper dataset, this field has not been well developed as other …

We consider the problem of training a deep neural network on a given classification task, eg,
ImageNet-1K (IN1K), so that it excels at that task as well as at other (future) transfer tasks …

To avoid the requirement of expert knowledge in conventional methods for car styling
analysis, this article proposes a machine learning–based method which requires no expert …