164 citations · 221 across the 5 of their papers we have counts for
9 papers
Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
Jihyeon Lee, Joseph Z. Xu, Kihyuk Sohn +8
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which in…
Creating High Resolution Images with a Latent Adversarial Generator
David Berthelot, Peyman Milanfar, Ian Goodfellow
Generating realistic images is difficult, and many formulations for this task have been proposed recently. If we restrict the task to that of generating a particular class of image…
Semi-Supervised Class Discovery
Jeremy Nixon, Jeremiah Liu, David Berthelot
One promising approach to dealing with datapoints that are outside of the initial training distribution (OOD) is to create new classes that capture similarities in the datapoints p…
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li +6
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combin…
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels
Shuang Song, David Berthelot, Afshin Rostamizadeh
We propose using active learning based techniques to further improve the state-of-the-art semi-supervised learning MixMatch algorithm. We provide a thorough empirical evaluation of…
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk +4
We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution al…