no code implementations • 7 Mar 2023 • David Berthelot, Arnaud Autef, Jierui Lin, Dian Ang Yap, Shuangfei Zhai, Siyuan Hu, Daniel Zheng, Walter Talbot, Eric Gu
Denoising Diffusion models have demonstrated their proficiency for generative sampling.
5 code implementations • ICLR 2022 • David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alex Kurakin
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 Domain Adaptation Unsupervised Domain Adaptation
no code implementations • 24 Nov 2020 • Jihyeon Lee, Joseph Z. Xu, Kihyuk Sohn, Wenhan Lu, David Berthelot, Izzeddin Gur, Pranav Khaitan, Ke-Wei, Huang, Kyriacos Koupparis, Bernhard Kowatsch
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 indicate what buildings and population centers have been most affected.
1 code implementation • ICLR 2020 • David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel
We improve the recently-proposed ``MixMatch semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring.
1 code implementation • 4 Mar 2020 • David Berthelot, Peyman Milanfar, Ian Goodfellow
That is to say, instead of generating an arbitrary image as a sample from the manifold of natural images, we propose to sample images from a particular "subspace" of natural images, directed by a low-resolution image from the same subspace.
no code implementations • 10 Feb 2020 • 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 previously rejected as uncategorizable.
27 code implementations • NeurIPS 2020 • Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.
1 code implementation • 2 Dec 2019 • Shuang Song, David Berthelot, Afshin Rostamizadeh
This analysis can be used to measure the relative value of labeled/unlabeled data at different points of the learning curve, where we find that although the incremental value of labeled data can be as much as 20x that of unlabeled, it quickly diminishes to less than 3x once more than 2, 000 labeled example are observed.
3 code implementations • 21 Nov 2019 • David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel
Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels.
no code implementations • 3 Sep 2019 • Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot
In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access.
30 code implementations • NeurIPS 2019 • David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel
Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.
no code implementations • ICLR 2019 • Ian Goodfellow, Yao Qin, David Berthelot
Current machine learning algorithms can be easily fooled by adversarial examples.
7 code implementations • ICLR 2019 • David Berthelot, Colin Raffel, Aurko Roy, Ian Goodfellow
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.
18 code implementations • 31 Mar 2017 • 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.
Ranked #68 on Image Generation on CIFAR-10 (Inception score metric)
13 code implementations • 3 Oct 2016 • Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, Rujun Long, Patrick McDaniel
An adversarial example library for constructing attacks, building defenses, and benchmarking both
2 code implementations • ACL 2016 • Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, David Berthelot
The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs).