11 citations · 53 across the 14 of their papers we have counts for
5 papers · 1 filter
ReConTab: Regularized Contrastive Representation Learning for Tabular Data
Suiyao Chen, Jing Wu, Naira Hovakimyan +1
Representation learning stands as one of the critical machine learning techniques across various domains. Through the acquisition of high-quality features, pre-trained embeddings s…
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing
Jing Wu, Naira Hovakimyan, Jennifer Hobbs
Classifying and segmenting patterns from a limited number of examples is a significant challenge in remote sensing and earth observation due to the difficulty in acquiring accurate…
Hallucination Improves the Performance of Unsupervised Visual Representation Learning
Jing Wu, Jennifer Hobbs, Naira Hovakimyan
Contrastive learning models based on Siamese structure have demonstrated remarkable performance in self-supervised learning. Such a success of contrastive learning relies on two co…
Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis
Jing Wu, David Pichler, Daniel Marley +3
A key challenge for much of the machine learning work on remote sensing and earth observation data is the difficulty in acquiring large amounts of accurately labeled data. This is…
Balanced Training for Sparse GANs
Yite Wang, Jing Wu, Naira Hovakimyan +1
Over the past few years, there has been growing interest in developing larger and deeper neural networks, including deep generative models like generative adversarial networks (GAN…