Compressing Generative Adversarial Neural Networks via Channel Pruning
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Solution Overview
Problem
Conventional noise-to-image Generative Adversarial Neural Networks (GANs) face inefficiencies in terms of storage space, computational resources, flexibility, and accuracy, particularly when implemented on mobile devices, due to their large size and complex architecture, leading to suboptimal performance in various computer vision tasks.
Innovation Solution
The proposed solution involves channel pruning and knowledge distillation to compress noise-to-image GANs, reducing network complexity and size while maintaining performance, by identifying informative channels and learning parameters to mimic the full-size GAN's outputs, enabling efficient and accurate operation on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional noise-to-image GANs are used to perform computer vision tasks, then image generation and editing performance is improved, but storage space requirements and computational costs increase substantially
Solution Approach 1:
The patent extracts and removes redundant channels from the GAN network architecture through channel pruning. By identifying and eliminating channels that contribute minimally to the final output, the system reduces the network size and storage requirements while preserving the essential functionality for high-quality image generation
Solution Approach 2:
The patent creates a compressed copy of the original GAN that mimics its behavior. Through knowledge distillation, the pruned network learns to replicate the output distributions of the full-size GAN, producing a smaller model that generates images with quality comparable to the original large model
2Adaptability or versatility
If conventional noise-to-image GANs are deployed on mobile devices, then local computer vision task performance is improved, but device memory and processing resources are overwhelmed
Solution Approach 1:
The patent removes computationally intensive components from the GAN architecture by pruning redundant channels. This extraction of unnecessary elements reduces the memory footprint and processing demands, enabling the network to run on mobile devices with limited resources while maintaining image generation capabilities
Solution Approach 2:
The patent changes the parameter configuration of the GAN by learning new weights for the pruned network through knowledge distillation. This parameter optimization allows the smaller network to achieve performance comparable to the full-size model, making mobile deployment feasible
3Productivity
If conventional compression techniques are applied to noise-to-image GANs, then model size is reduced, but generation accuracy and performance deteriorate noticeably
Solution Approach 1:
The patent implements feedback through the knowledge distillation process, where the pruned network's outputs are continuously compared against the full-size GAN's outputs. This feedback loop guides the optimization of the pruned network's parameters, ensuring that accuracy is maintained despite the reduction in model size
Solution Approach 2:
The patent uses the full-size GAN as an intermediary teacher model during the compression process. The teacher model provides target output distributions that guide the training of the student (pruned) model, enabling the smaller network to learn accurate image generation without directly inheriting the large model's parameters
Data Source
AI summary
This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that utilize channel pruning and knowledge distillation to generate a compact noise-to-image GAN. For example, the disclosed systems prune less informative channels via outgoing channel weights of the GAN. In some implementations, the disclosed systems further utilize content-aware pruning by utilizing a differentiable loss between an image generated by the GAN and a modified version of the image to identify sensitive channels within the GAN during channel pruning. In some embodiments, the disclosed systems utilize knowledge distillation to learn parameters for the pruned GAN to mimic a full-size GAN. In certain implementations, the disclosed systems utilize content-aware knowledge distillation by applying content masks on images generated by both the pruned GAN and its full-size counterpart to obtain knowledge distillation losses between the images for use in learning the parameters for the pruned GAN.