Jindong Wang

Visual Domain Adaptation with Manifold Embedded Distribution Alignment

2018 · arXiv (Cornell University)

Visual domain adaptation aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning. However, there …

Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning

2019

Deep unsupervised domain adaptation (UDA) has recently received increasing attention from researchers. However, existing methods are computationally intensive due to the computation cost of Convolutional Neural Networks (CNN) adopted by most work. To date, there …

MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition

2021 · arXiv (Cornell University)

In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking a weighted combination of two different speech …

ReMoS

2022 · Proceedings of the 44th International Conference on Software Engineering

Transfer learning is a popular software reuse technique in the deep learning community that enables developers to build custom models (students) based on sophisticated pretrained models (teachers). However, like vulnerability inheritance in traditional software reuse, …

SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning

2023 · arXiv (Cornell University)

The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance. In this paper, we first revisit the popular pseudo-labeling …

A Survey on Evaluation of Large Language Models

2024 · ACM Transactions on Intelligent Systems and Technology

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, …