TL;DR
This paper introduces MANAS, a neural architecture search method that learns to assemble reusable modules into adaptive models for different tasks, inspired by human skill learning and reuse, leading to improved performance over static architectures.
Contribution
We propose MANAS, a novel modularized adaptive neural architecture search method that enables learning and reusing basic skills for various tasks, enhancing flexibility and efficiency.
Findings
MANAS outperforms static architectures on multiple datasets.
Adaptive assembly of modules improves task-specific performance.
Empirical analysis confirms the effectiveness of the proposed method.
Abstract
Human intelligence is able to first learn some basic skills for solving basic problems and then assemble such basic skills into complex skills for solving complex or new problems. For example, the basic skills "dig hole," "put tree," "backfill" and "watering" compose a complex skill "plant a tree". Besides, some basic skills can be reused for solving other problems. For example, the basic skill "dig hole" not only can be used for planting a tree, but also can be used for mining treasures, building a drain, or landfilling. The ability to learn basic skills and reuse them for various tasks is very important for humans because it helps to avoid learning too many skills for solving each individual task, and makes it possible to solve a compositional number of tasks by learning just a few number of basic skills, which saves a considerable amount of memory and computation in the human brain.…
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