Learnable Sampling Layers for Neural Network Architecture Adaptation
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Solution Overview
Problem
Conventional automated machine learning systems for neural networks are inflexible, inaccurate, and inefficient, often requiring significant computational resources and human intervention, limiting their ability to adapt to different tasks and operate effectively on mobile devices.
Innovation Solution
The introduction of learnable sampling layers that utilize a shape adaptor to automatically adjust the neural network architecture by learning a scaling factor through back-propagation and end-to-end optimization, allowing the network to adapt its structure for specific tasks without human input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional automated machine learning systems use fixed neural network architectures, then the system structure is simple and easy to implement, but the system lacks flexibility and cannot adapt to different tasks
Solution Approach 1:
The patent applies dynamics by transforming the fixed neural network architecture into a dynamic, learnable structure. The architecture is parameterized using continuous relaxation, allowing the network to automatically adapt its structure (number of layers, filters, kernel sizes) to different tasks through learning, rather than being statically defined. This enables the system to flexibly adjust architectural parameters based on task requirements while maintaining a unified implementation framework.
2Extent of automation
If conventional systems rely on human expertise to design neural network architectures, then the architecture can be optimized for specific tasks, but the system requires significant human effort and expert knowledge
Solution Approach 1:
The patent implements self-service by enabling the neural network architecture to automatically design and optimize itself for different tasks. The system uses continuous relaxation and learnable parameters to allow the network to autonomously determine optimal architectural configurations (number of layers, filter counts, kernel sizes) based on the task at hand, eliminating the need for manual human intervention in architecture design while reducing reliance on expert knowledge.
3Manufacturing precision
If conventional NAS approaches learn optimal operations, then the network operations are optimized, but the computational resources and time required are extremely large
Solution Approach 1:
The patent applies parameter changes by transforming discrete architectural decisions (number of layers, filter counts, kernel sizes) into continuous learnable parameters through relaxation. This allows the system to optimize network operations and architecture simultaneously using gradient-based methods, achieving operation optimization without the exhaustive search required by conventional NAS approaches, thereby significantly reducing computational energy consumption and training time.
4Adaptability or versatility
If conventional systems use fixed neural network architectures, then the implementation is efficient and fast, but the system cannot flexibly adapt the structure where needed
Solution Approach 1:
The patent applies dynamics by creating a learnable, adaptive architecture where parameters such as the number of layers, filters, and kernel sizes can be automatically adjusted based on task requirements. The continuous relaxation approach allows these architectural parameters to be optimized through gradient descent, enabling flexible adaptation while maintaining implementation efficiency through automated learning rather than manual reconfiguration.
5Reliability
If conventional NAS algorithms are used to discover optimal operations, then the network performance is improved, but the training time equals that required to train many neural networks in parallel
Solution Approach 1:
The patent applies parameter changes by converting discrete architectural hyperparameters into continuous learnable parameters through relaxation. This transformation enables the system to optimize both network operations and architecture simultaneously using efficient gradient-based optimization methods, achieving high network performance accuracy without requiring the extensive parallel training time associated with conventional NAS algorithms that search through discrete architectural configurations.
Data Source
AI summary
The present disclosure relates to shaping the architecture of a neural network. For example, the disclosed systems can provide a neural network shaping mechanism for at least one sampling layer of a neural network. The neural network shaping mechanism can include a learnable scaling factor between a sampling rate of the at least one sampling layer and an additional sampling function. The disclosed systems can learn the scaling factor based on a dataset while jointly learning the network weights of the neural network. Based on the learned scaling factor, the disclosed systems can shape the architecture of the neural network by modifying the sampling rate of the at least one sampling layer.