Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
| Autor: | Baz, Adrian El, Ullah, Ihsan, Alcobaça, Edesio, Carvalho, André C. P. L. F., Chen, Hong, Ferreira, Fabio, Gouk, Henry, Guan, Chaoyu, Guyon, Isabelle, Hospedales, Timothy, Hu, Shell, Huisman, Mike, Hutter, Frank, Liu, Zhengying, Mohr, Felix, Öztürk, Ekrem, van Rijn, Jan N., Sun, Haozhe, Wang, Xin, Zhu, Wenwu |
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| Přispěvatelé: | Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Computer Science - Neural and Evolutionary Computing [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Machine Learning (cs.LG) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Automated Machine Learning Artificial Intelligence (cs.AI) meta-learning [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] challenge few-shot learning Neural and Evolutionary Computing (cs.NE) competition |
| Zdroj: | NeurIPS 2021 Competition and Demonstration Track NeurIPS 2021 Competition and Demonstration Track, Dec 2021, On-line, United States |
| Popis: | Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available. Metalearning methods can address this problem by transferring knowledge from related tasks, thus reducing the amount of data and computing resources needed to learn new tasks. We organize the MetaDL competition series, which provide opportunities for research groups all over the world to create and experimentally assess new meta-(deep)learning solutions for real problems. In this paper, authored collaboratively between the competition organizers and the top-ranked participants, we describe the design of the competition, the datasets, the best experimental results, as well as the top-ranked methods in the NeurIPS 2021 challenge, which attracted 15 active teams who made it to the final phase (by outperforming the baseline), making over 100 code submissions during the feedback phase. The solutions of the top participants have been open-sourced. The lessons learned include that learning good representations is essential for effective transfer learning. version 2 is the correct version, including supplementary material at the end |
| Databáze: | OpenAIRE |
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