As-Light-As-Possible Autoencoder Pruning L1-Norm
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Solution Overview
Problem
Current neural network optimization techniques for photo-realistic image generation are inefficient, as they often require high GPU costs, result in slow inference, and deteriorate image quality and resolution, especially on edge devices, due to their generic nature and lack of device-specific optimization.
Innovation Solution
The development of an 'as-light-as-possible' autoencoder (ALAP-AE) neural network is achieved by pruning filters from an initial AE neural network based on a L1-norm loss function and retraining using a student-teacher model, resulting in a lightweight architecture optimized for specific compute environments with minimal image quality loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic neural network optimization techniques are used, then image generation capability is achieved, but computational cost and complexity are high
Solution Approach 1:
The patent extracts and removes redundant filters from the neural network based on L1-norm loss function computation. Filters with small L1-norm values are identified as redundant and pruned from the network, reducing computational complexity while maintaining essential image generation capabilities
Solution Approach 2:
The patent changes the parameter configuration by applying L1-norm regularization to drive filter weights toward zero. This parameter transformation identifies which filters can be safely removed, enabling the network to operate with fewer filters and reduced complexity
2Device complexity
If network compression techniques are applied, then computational cost is reduced, but image quality deteriorates
Solution Approach 1:
The patent employs feedback through L1-norm loss function computation during the pruning process. The loss function provides continuous feedback on the impact of filter removal, allowing the system to adjust which filters are pruned to minimize image quality degradation while reducing computational cost
Solution Approach 2:
The patent applies partial pruning by removing only the filters with the smallest L1-norm values rather than pruning all filters uniformly. This selective partial action maintains the essential filters needed for image quality while removing redundant ones to reduce computational cost
3Productivity
If device-specific optimization is implemented, then inference speed improves, but optimization process becomes more complex
Solution Approach 1:
The patent implements self-service optimization where the neural network automatically identifies and prunes its own redundant filters based on L1-norm computation. This self-organizing process adapts the network to the target device's computational constraints without requiring manual device-specific configuration, simplifying the optimization process while improving inference speed
Data Source
AI summary
A computer system (which includes one or more computers) that generates a second autoencoder (AE) neural network (such as an ALAP-AE neural network) is described. During operation, the computer system may obtain information specifying an initial AE neural network. Then, the computer system may compute a subset of filters associated with the initial AE neural network to remove based at least in part on a L1-norm loss function and weights associated with filters in initial AE neural network. Moreover, the computer system may prune the subset of the filters from the initial AE neural network. Next, the computer system may generate the ALAP-AE neural network by retraining the initial AE neural network, where the retraining includes a student-teacher model in which the teacher includes the pruned initial AE neural network and the student includes the ALAP-AE neural network.