Self-Supervised Neural Architecture Search
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Solution Overview
Problem
Current neural architecture search techniques require human-annotated training data and are cumbersome, time-consuming, and expert-dependent, especially for developing neural networks to perform specific tasks like monocular depth estimation.
Innovation Solution
A neural architecture search system that uses self-supervised training, where a controller neural network generates candidate neural networks based on model weights, performs tasks using training data, determines loss values, and adjusts weights without annotated supervision, enabling automated generation of neural networks ready for implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised training with human-annotated training data is used, then the neural network can be trained to perform specific tasks, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system employs self-supervised training where the neural network generates its own training data and supervision signals automatically without human annotation. The controller neural network autonomously creates candidate neural networks, generates training data, and adjusts weights based on self-evaluated performance, eliminating the need for cumbersome human-annotated datasets while maintaining training effectiveness
Solution Approach 2:
The controller neural network pre-generates candidate neural networks and training data before the actual training process. By preparing candidate architectures and training data in advance through automated generation, the system eliminates the time-consuming process of manual data annotation and network design while ensuring ready-to-train configurations
2Manufacturing precision
If expert developers manually design neural networks, then the network architecture can be optimized for specific tasks, but the development process becomes painstakingly slow
Solution Approach 1:
The system replaces the manual mechanical process of expert network design with an automated computational system. The controller neural network uses algorithmic weight adjustment based on self-supervised training to automatically optimize architecture and parameters, substituting human expert manual tuning with automated machine-driven optimization that is both precise and rapid
Solution Approach 2:
The controller neural network autonomously performs architecture search and optimization without human intervention. It automatically generates candidate networks, evaluates their performance on self-generated training data, and iteratively improves architectures through self-directed weight adjustment, achieving both optimization quality and high development speed
3Extent of automation
If reinforcement learning is used to generate model descriptions, then automated neural network generation is achieved, but the system requires complex supervised training with annotated data
Solution Approach 1:
The system achieves automation while eliminating the need for annotated training data by implementing self-supervised training. The controller neural network generates its own training data and supervision signals automatically, maintaining high automation levels while removing the complex requirement for human-annotated datasets that would otherwise be needed
Data Source
AI summary
A neural architecture search system for generating a neural network includes one or more processors and a memory. The memory includes a generator module, a self-supervised training module, and an output module. The modules cause the one or more processors to generate a candidate neural network by a controller neural network, obtain training data, generate an output by the candidate neural network performing a specific task using the training data as an input, determine a loss value using a loss function that considers the output of the candidate neural network and at least a portion of the training data, adjust the one or more model weights of the controller neural network based on the loss value, and output the candidate neural network. The candidate neural network may be derived from the controller neural network and one or more model weights of the controller neural network.