RESEARCH
COMPUTER VISION
Adversarial Continual Learning
July 17, 2020
Abstract
Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demonstrate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at https://github.com/facebookresearch/Adversarial-Continual-Learning
AUTHORS
Written by
Marcus Rohrbach
Franziska Meier
Roberto Calandra
Sayna Ebrahimi
Trevor Darrell
Publisher
ECCV
Research Topics
Related Publications
May 12, 2026
HUMAN & MACHINE INTELLIGENCE
RESEARCH
NeuralSet: A High-Performing Python Package for Neuro-AI
Corentin Bel, Linnea Evanson, Julien Gadonneix, 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, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 12, 2026
May 06, 2026
HUMAN & MACHINE INTELLIGENCE
RESEARCH
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
Saarang Panchavati, Antoine Ratouchniak, Mingfang (Lucy) Zhang, Elisa Cascardi, Hubert Banville, Jarod Levy, Jean-Rémi King, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 06, 2026
April 16, 2026
RESEARCH
AIRA₂: Overcoming Bottlenecks in AI Research Agents
Nicola Cancedda, Pontus Stenetorp, Alexis Audran-Reiss, Alisia Lupidi, Anton Protopopov, Bassel Al Omari, Carole-Jean Wu, Derek Dunfield, Despoina Magka, Edan Toledo, Hela Momand, Ishita Mediratta, Jakob Foerster, Jean-Christophe Gagnon-Audet, Karen Hambardzumyan, Kelvin Niu, Martin Josifoski, Michael Kuchnik, Michael Shvartsman, Nicolas Baldwin, Parth Pathak, Rishi Hazra, Tatiana Shavrina, Thomas Simon Foster, Yoram Bachrach
April 16, 2026
April 14, 2026
COMPUTER VISION
ML APPLICATIONS
TransText: Transparency Aware Image-to-Video Typography Animation
Zijian Zhou, Bohao Tang, Pengfei Liu, Fei Zhang, Frost Xu, Hang Li (BizAI), Semih Gunel, Sen He, Soubhik Sanyal, Tao Xiang, Viktar Atliha, Zhe Wang
April 14, 2026
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.
Our approach
Latest news
Foundational models
Meta © 2026