Core Machine Learning
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
July 18, 2021
Abstract
Convolutional architectures have proven extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially lower performance ceiling. Vision Transformers (ViTs) rely on more flexible self-attention layers, and have recently outperformed CNNs for image classification. However, they require costly pre-training on large external datasets or distillation from pretrained convolutional networks. In this paper, we ask the following question: is it possible to combine the strengths of these two architectures while avoiding their respective limitations? To this end, we introduce gated positional self-attention (GPSA), a form of positional self-attention which can be equipped with a “soft” convolutional inductive bias. We initialize the GPSA layers to mimic the locality of convolutional layers, then give each attention head the freedom to escape locality by adjusting a gating parameter regulating the attention paid to position versus content information. The resulting convolutional-like ViT architecture, ConViT, outperforms the DeiT (Touvron et al., 2020) on ImageNet, while offering a much improved sample efficiency. We further investigate the role of locality in learning by first quantifying how it is encouraged in vanilla self-attention layers, then analyzing how it is escaped in GPSA layers. We conclude by presenting various ablations to better understand the success of the ConViT. Our code and models are released publicly at https://github.com/facebookresearch/convit.
AUTHORS
Written by
Stephane d’Ascoli
Hugo Touvron
Matthew L. Leavitt
Ari S. Morcos
Giulio Biroli
Levent Sagun
Publisher
ICML 2021
Research Topics
Core Machine Learning
Related Publications
May 12, 2026
Human & Machine Intelligence
NeuralSet: A High-Performing Python Package for Neuro-AI
Jean Remi King, Corentin Bel, Linnea Evanson, Julien Gadonneix, Sophia Houhamdi, Jarod Levy, Josephine Raugel, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Teon Brooks, Katelyn Begany, Yohann Benchetrit, Marlene Careil, Hubert Jacob Banville, Stéphane d'Ascoli, Simon Dahan, Jérémy Rapin
May 12, 2026
November 18, 2025
Core Machine Learning
Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance
Shalini Maiti *, Amar Budhiraja *, Bhavul Gauri, Gaurav Chaurasia, Anton Protopopov, Alexis Audran-Reiss, Michael Slater, Despoina Magka, Tatiana Shavrina, Roberta Raileanu, Yoram Bachrach, * Equal authorship
November 18, 2025
October 13, 2025
Reinforcement Learni9ng
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang, Siyan Zhao, Cai Zhou, Shannon Zejiang Shen, Feiyu Chen, Tommi Jaakkola, Yuandong Tian, Bo Liu
October 13, 2025
September 24, 2025
NLP
CWM: An Open-Weights LLM for Research on Code Generation with World Models
Jade Copet, Quentin Carbonneaux, Gal Cohen, Jonas Gehring, Jacob Kahn, Jannik Kossen, Felix Kreuk, Emily McMilin, Michel Meyer, Yuxiang Wei, David Zhang, Kunhao Zheng, Jordi Armengol-Estape, Pedram Bashiri, Maximilian Beck, Pierre Chambon, Abhishek Charnalia, Chris Cummins, Juliette Decugis, Zacharias Fisches, François Fleuret, Fabian Gloeckle, Alex Gu, Michael Hassid, Daniel Haziza, Badr Youbi Idrissi, Christian Keller, Rahul Kindi, Hugh Leather, Gallil Maimon, Aram Markosyan, Francisco Massa, Pierre-Emmanuel Mazaré, Vegard Mella, Naila Murray, Keyur Muzumdar, Peter O'Hearn, Matteo Pagliardini, Dmitrii Pedchenko, Tal Remez, Volker Seeker, Marco Selvi, Oren Sultan, Sida Wang, Luca Wehrstedt, Ori Yoran, Lingming Zhang, Taco Cohen, Yossi Adi, Gabriel Synnaeve
September 24, 2025
December 07, 2020
Core Machine Learning
Adversarial Example Games
Avishek Joey Bose, Gauthier Gidel, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton
December 07, 2020
November 03, 2020
Core Machine Learning
Robust Embedded Deep K-means Clustering
Rui Zhang, Hanghang Tong Yinglong Xia, Yada Zhu
November 03, 2020
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Meta © 2026
