Taskonomy: Disentangling Task Transfer Learning
| Autor: | Leonidas J. Guibas, Jitendra Malik, Alexander Sax, William B. Shen, Amir Roshan Zamir, Silvio Savarese |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Artificial Intelligence Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences Reuse Machine learning computer.software_genre 01 natural sciences Machine Learning (cs.LG) Computer Science - Robotics 0202 electrical engineering electronic engineering information engineering Neural and Evolutionary Computing (cs.NE) 0105 earth and related environmental sciences business.industry Computer Science - Neural and Evolutionary Computing Solver Computer Science - Learning Artificial Intelligence (cs.AI) Task analysis Labeled data 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business Robotics (cs.RO) computer Intuition |
| Zdroj: | CVPR |
| Popis: | Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks. This is done via finding (first and higher-order) transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks in a latent space. The product is a computational taxonomic map for task transfer learning. We study the consequences of this structure, e.g. nontrivial emerged relationships, and exploit them to reduce the demand for labeled data. For example, we show that the total number of labeled datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3 (compared to training independently) while keeping the performance nearly the same. We provide a set of tools for computing and probing this taxonomical structure including a solver that users can employ to devise efficient supervision policies for their use cases. CVPR 2018 (Oral). See project website and live demos at http://taskonomy.vision/ |
| Databáze: | OpenAIRE |
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