Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

Deep neural networks demonstrated their ability to provide remarkable performances on a
wide range of supervised learning tasks (eg, image classification) when trained on extensive …

Abstract Generative Adversarial Network (GAN) is a research hotspot in deep generative
models, which has been widely used in the field of medical image fusion. This paper …

Despite rapid progress in the recent past, current speech recognition systems still require
labeled training data which limits this technology to a small fraction of the languages spoken …

The main challenge in the field of unsupervised machine translation (UMT) is to associate
source-target sentences in the latent space. As people who speak different languages share …

Video anomaly detection is well investigated in weakly supervised and one-class
classification (OCC) settings. However, unsupervised video anomaly detection is quite …

We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet,
which is 2.0% better than the state-of-the-art model that requires 3.5 B weakly labeled …

From AlphaGo to ChatGPT, the field of AI has launched a series of remarkable
achievements in recent years. Analyzing, comparing, and summarizing these achievements …

Semi-supervised learning lately has shown much promise in improving deep learning
models when labeled data is scarce. Common among recent approaches is the use of …

Metaverse and intelligent transportation system (ITS) are disruptive technologies that have
the potential to transform the current transportation system by decreasing traffic accidents …