no code implementations • 22 Mar 2024 • Teresa Yeo, Andrei Atanov, Harold Benoit, Aleksandr Alekseev, Ruchira Ray, Pooya Esmaeil Akhoondi, Amir Zamir
In this work, we present a method to control a text-to-image generative model to produce training data specifically "useful" for supervised learning.
no code implementations • NeurIPS 2023 • David Mizrahi, Roman Bachmann, Oğuzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, Amir Zamir
Current machine learning models for vision are often highly specialized and limited to a single modality and task.
no code implementations • ICCV 2023 • Teresa Yeo, Oğuzhan Fatih Kar, Zahra Sodagar, Amir Zamir
We propose a method for adapting neural networks to distribution shifts at test-time.
no code implementations • 1 Dec 2022 • Andrei Atanov, Andrei Filatov, Teresa Yeo, Ajay Sohmshetty, Amir Zamir
An intriguing question would be: what if, instead of fixing the task and searching in the model space, we fix the model and search in the task space?
1 code implementation • CVPR 2022 • Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov, Amir Zamir
We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks.
no code implementations • ICCV 2021 • Teresa Yeo, Oğuzhan Fatih Kar, Alexander Sax, Amir Zamir
We present a method for making neural network predictions robust to shifts from the training data distribution.
no code implementations • 1 Jan 2021 • Teresa Yeo, Oguzhan Fatih Kar, Amir Zamir
We present a method for making predictions using neural networks that, at the test time, is robust against shifts from the training data distribution.
1 code implementation • 7 Jun 2020 • Amir Zamir, Alexander Sax, Teresa Yeo, Oğuzhan Kar, Nikhil Cheerla, Rohan Suri, Zhangjie Cao, Jitendra Malik, Leonidas Guibas
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
no code implementations • 8 Nov 2018 • Teresa Yeo, Parameswaran Kamalaruban, Adish Singla, Arpit Merchant, Thibault Asselborn, Louis Faucon, Pierre Dillenbourg, Volkan Cevher
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students.