Guided CoModGaN Channel Pruning for Mobile Inference
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Solution Overview
Problem
Conventional image processing models, such as generative adversarial networks (GANs), face high computational and memory demands, making them unsuitable for deployment on mobile devices.
Innovation Solution
Implement channel pruning and tensor decomposition techniques on image generation networks like CoModGAN to optimize model size and inference time, reducing memory usage and computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional image generation networks are used, then image generation capability is maintained, but model size and memory usage are excessive for mobile devices
Solution Approach 1:
The patent extracts and removes redundant channels from the encoder and decoder networks based on importance metrics (L1 norm, gradient magnitude, correlation analysis). This extraction process eliminates unnecessary computational elements while preserving the core functional channels, achieving model compression without significant capability loss
Solution Approach 2:
The patent changes the parameter configuration by dynamically adjusting which channels are active based on importance metrics. Different pruning strategies (L1 norm-based, gradient-based, correlation-based) modify channel parameters differently, allowing the model to adapt its structure for mobile deployment while maintaining generation quality
2Productivity
If conventional image generation networks are used, then image quality is maintained, but inference time is too slow for mobile devices
Solution Approach 1:
By extracting and removing redundant channels from both encoder and decoder, the patent reduces the computational workload per inference. The pruned network processes fewer channels while maintaining critical information flow, directly improving inference speed for mobile devices
Solution Approach 2:
The patent applies partial pruning where only the most important channels are retained rather than processing all channels equally. This partial action approach maintains sufficient image quality by keeping essential channels while eliminating redundant computations
3Quantity of substance
If channel pruning is applied to encoder, then model size is reduced, but decoder performance may be affected
Solution Approach 1:
The patent merges the pruning operations of encoder and decoder by applying coordinated channel pruning to both networks simultaneously. The skip connections are preserved and integrated into the pruned structure, ensuring that encoder and decoder work together harmoniously with reduced model size
Solution Approach 2:
The patent employs feedback mechanisms where the pruning of encoder channels informs the pruning strategy for decoder channels. Importance metrics calculated from encoder pruning results are used to guide decoder pruning, ensuring that decoder performance is maintained through informed channel selection
Data Source
AI summary
Methods for image processing are described. Embodiments of the present disclosure identifies an image generation network that includes an encoder and a decoder; prunes channels of a block of the encoder; prunes channels of a block of the decoder that is connected to the block of the encoder by a skip connection, wherein the channels of the block of the decoder are pruned based on the pruned channels of the block of the encoder; and generates an image using the image generation network based on the pruned channels of the block of the encoder and the pruned channels of the block of the decoder.