GAN Channel Pruning With Knowledge Distillation for Image Compression
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Solution Overview
Problem
Generative Adversarial Networks (GANs) face challenges with high computational cost and bulky memory usage, limiting their efficiency in generating high-fidelity images.
Innovation Solution
An image compression system using a teacher network with inception-based residual blocks and a one-step pruning method to train a student network, maximizing feature similarity through knowledge distillation, resulting in a more efficient student network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard GAN architecture is used to generate high-fidelity images, then image quality is improved, but computational cost and memory usage increase
Solution Approach 1:
The GAN architecture is segmented into a teacher network and a student network. The teacher network uses a standard architecture for high-quality image generation, while the student network uses a compressed architecture for efficient inference. This segmentation allows the system to maintain high image quality through the teacher network while achieving low computational cost through the student network.
Solution Approach 2:
A student network is created as a compressed copy of the teacher network. The student network replicates the essential functionality of the teacher network but with reduced computational requirements through pruning and compression techniques, enabling deployment on resource-constrained devices while maintaining acceptable image quality.
2Measurement precision
If a standard GAN architecture is used to generate high-fidelity images, then image quality is improved, but memory usage increases
Solution Approach 1:
The model is segmented into teacher and student components, allowing the memory-intensive teacher network to be used only during the training phase for achieving high image quality, while the compressed student network is deployed for inference to reduce memory usage during actual operation.
Solution Approach 2:
A compressed copy of the teacher network is created as the student network. This copy retains the essential functionality but with significantly reduced memory footprint through pruning operations that remove redundant parameters and connections, enabling deployment on devices with limited memory resources.
3Productivity
If network compression is applied to reduce computational cost, then efficiency is improved, but image generation quality may deteriorate
Solution Approach 1:
The teacher network is trained first to achieve high image quality before the student network is created. This preliminary training establishes a high-quality reference model that guides the subsequent compression process, ensuring that the student network learns to preserve important features while reducing computational cost.
Solution Approach 2:
The teacher network acts as an intermediary that transfers knowledge to the student network through knowledge distillation. This intermediary process allows the student network to learn from the teacher's high-quality image generation capabilities while maintaining its compressed architecture, thus preserving image quality despite reduced computational requirements.
Data Source
AI summary
Systems and methods herein describe an image compression system. The image compression system generates a first generative adversarial network (GAN), identifies a threshold, based on the threshold, generates a second GAN by pruning channels of the first GAN, trains the second GAN using similarity-based knowledge distillation from the first GAN, and stores the trained second GAN.