Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Bardes, Adrien"'
Autor:
Bordes, Florian, Pang, Richard Yuanzhe, Ajay, Anurag, Li, Alexander C., Bardes, Adrien, Petryk, Suzanne, Mañas, Oscar, Lin, Zhiqiu, Mahmoud, Anas, Jayaraman, Bargav, Ibrahim, Mark, Hall, Melissa, Xiong, Yunyang, Lebensold, Jonathan, Ross, Candace, Jayakumar, Srihari, Guo, Chuan, Bouchacourt, Diane, Al-Tahan, Haider, Padthe, Karthik, Sharma, Vasu, Xu, Hu, Tan, Xiaoqing Ellen, Richards, Megan, Lavoie, Samuel, Astolfi, Pietro, Hemmat, Reyhane Askari, Chen, Jun, Tirumala, Kushal, Assouel, Rim, Moayeri, Mazda, Talattof, Arjang, Chaudhuri, Kamalika, Liu, Zechun, Chen, Xilun, Garrido, Quentin, Ullrich, Karen, Agrawal, Aishwarya, Saenko, Kate, Celikyilmaz, Asli, Chandra, Vikas
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce
Externí odkaz:
http://arxiv.org/abs/2405.17247
Autor:
Garrido, Quentin, Assran, Mahmoud, Ballas, Nicolas, Bardes, Adrien, Najman, Laurent, LeCun, Yann
Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model. While previously limited to predicting missing parts of an input, we explore how to generalize the JEPA predic
Externí odkaz:
http://arxiv.org/abs/2403.00504
Autor:
Bardes, Adrien, Garrido, Quentin, Ponce, Jean, Chen, Xinlei, Rabbat, Michael, LeCun, Yann, Assran, Mahmoud, Ballas, Nicolas
This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encod
Externí odkaz:
http://arxiv.org/abs/2404.08471
Autor:
Goldblum, Micah, Souri, Hossein, Ni, Renkun, Shu, Manli, Prabhu, Viraj, Somepalli, Gowthami, Chattopadhyay, Prithvijit, Ibrahim, Mark, Bardes, Adrien, Hoffman, Judy, Chellappa, Rama, Wilson, Andrew Gordon, Goldstein, Tom
Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an ImageNet-trained convolutional neural network. However, the recent pa
Externí odkaz:
http://arxiv.org/abs/2310.19909
Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, and focus on identifying and differentiating objects in images and videos. On the other hand, optical f
Externí odkaz:
http://arxiv.org/abs/2307.12698
Autor:
Balestriero, Randall, Ibrahim, Mark, Sobal, Vlad, Morcos, Ari, Shekhar, Shashank, Goldstein, Tom, Bordes, Florian, Bardes, Adrien, Mialon, Gregoire, Tian, Yuandong, Schwarzschild, Avi, Wilson, Andrew Gordon, Geiping, Jonas, Garrido, Quentin, Fernandez, Pierre, Bar, Amir, Pirsiavash, Hamed, LeCun, Yann, Goldblum, Micah
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art with a high barrier to entry. While many components are familiar, succes
Externí odkaz:
http://arxiv.org/abs/2304.12210
Autor:
Bendidi, Ihab, Bardes, Adrien, Cohen, Ethan, Lamiable, Alexis, Bollot, Guillaume, Genovesio, Auguste
Self-supervised representation learning in computer vision relies heavily on hand-crafted image transformations to learn meaningful and invariant features. However few extensive explorations of the impact of transformation design have been conducted
Externí odkaz:
http://arxiv.org/abs/2304.11718
Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features. The former works best for classification tasks while the latter is bes
Externí odkaz:
http://arxiv.org/abs/2210.01571
One unexpected technique that emerged in recent years consists in training a Deep Network (DN) with a Self-Supervised Learning (SSL) method, and using this network on downstream tasks but with its last few projector layers entirely removed. This tric
Externí odkaz:
http://arxiv.org/abs/2206.13378
Self-supervised learning (SSL) has recently achieved tremendous empirical advancements in learning image representation. However, our understanding of the principle behind learning such a representation is still limited. This work shows that joint-em
Externí odkaz:
http://arxiv.org/abs/2206.08954