2.3k citations · 4.4k across the 14 of their papers we have counts for
11 papers · 1 filter
TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation
David Berthelot, Arnaud Autef, Jierui Lin +6
Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques su…
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
David Berthelot, Rebecca Roelofs, Kihyuk Sohn +2
We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one…
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…
High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski, Nicholas Carlini, David Berthelot +2
In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access. We taxonomize model extraction attacks aroun…
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow +3
Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current d…
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer
David Berthelot, Colin Raffel, Aurko Roy +1
Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some ca…
BEGAN: Boundary Equilibrium Generative Adversarial Networks
David Berthelot, Thomas Schumm, Luke Metz
We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks. This method…
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini +23
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be us…