Teachers Do More Than Teach: Compressing Image-to-Image Models (2103.03467v2)

Abstract: Generative Adversarial Networks (GANs) have achieved huge success in generating high-fidelity images, however, they suffer from low efficiency due to tremendous computational cost and bulky memory usage. Recent efforts on compression GANs show noticeable progress in obtaining smaller generators by sacrificing image quality or involving a time-consuming searching process. In this work, we aim to address these issues by introducing a teacher network that provides a search space in which efficient network architectures can be found, in addition to performing knowledge distillation. First, we revisit the search space of generative models, introducing an inception-based residual block into generators. Second, to achieve target computation cost, we propose a one-step pruning algorithm that searches a student architecture from the teacher model and substantially reduces searching cost. It requires no l1 sparsity regularization and its associated hyper-parameters, simplifying the training procedure. Finally, we propose to distill knowledge through maximizing feature similarity between teacher and student via an index named Global Kernel Alignment (GKA). Our compressed networks achieve similar or even better image fidelity (FID, mIoU) than the original models with much-reduced computational cost, e.g., MACs. Code will be released at https://github.com/snap-research/CAT.

• The paper introduces a teacher-student framework with a one-step pruning algorithm that achieves a search process over 10,000 times faster than conventional methods.
• The paper employs kernel alignment for knowledge distillation to directly maximize feature similarity, preserving key metrics like FID and mIoU.
• The approach significantly reduces computational costs, making it ideal for real-time deployment of high-performance GANs in resource-limited environments.

Analysis of "Teachers Do More Than Teach: Compressing Image-to-Image Models"

The paper "Teachers Do More Than Teach: Compressing Image-to-Image Models" presents an innovative approach to address the significant computational cost associated with Generative Adversarial Networks (GANs) used for image generation tasks. By employing a teacher-student framework, the authors develop a method to compress image-to-image models effectively. This essay dissects the methodologies proposed and their implications for the field.

GANs have become a cornerstone in generating high-quality images, yet their application is often limited by maintenance costs related to computational demands and memory usage. Traditional approaches to model compression have struggled, either by compromising image quality or resorting to lengthy and resource-intensive search processes. This paper contributes a distinctive methodology, integrating the strengths of network architecture and knowledge distillation.

Key Methodological Contributions

1. Teacher Network as Search Space and Guide: The authors propose a teacher network which not only serves the typical role in knowledge distillation but also provides a substantial architectural search space. The network design integrates inception-based residual blocks, adding robustness and flexibility, and enabling efficient generator designs for student models while maintaining high image fidelity metrics, such as Fréchet Inception Distance (FID) and mean Intersection over Union (mIoU).
2. One-Step Pruning Algorithm: A pivotal technique introduced is an efficient one-step pruning algorithm that streamlines the search for a student architecture from the teacher model. Notably, the method circumvents the complexity of $\ell^1$ sparsity regularization, accelerating the search process by over 10,000 times compared to baseline methods such as those by Li et al. The student network is pruned directly from the pre-trained teacher without requiring an additional supernet, reducing the need for manual hyperparameter adjustment and computational resources.
3. Kernel Alignment for Knowledge Distillation: The paper embraces a novel distillation approach using Kernel Alignment (KA) to directly maximize feature similarity between teacher and student models. Unlike response-based or traditional feature-based distillation, the use of KA avoids the necessity for extra learnable layers. This contributes to a more effective transfer of learned representations across different architectures.

Empirical Results

The experimental evaluation benchmarks the compressed models against both original models and state-of-the-art GAN compression techniques over several datasets, including Horse→Zebra with CycleGAN, and Cityscapes with Pix2pix and GauGAN. These results underscore the efficacy of the method through:

• Significant reductions in Multiply-Accumulate Operations (MACs) while achieving or surpassing original model performance metrics. For CycleGAN on the Horse→Zebra dataset, the method reduces MACs by 22.2 times while improving the FID from 61.53 to 53.48.
• Enhanced performance of compressed models, which are crucial for deploying GANs in real-time settings on devices with limited resources.

Practical and Theoretical Implications

This research holds substantial implications for practical applications, particularly in deploying deep models on devices where computational efficiency is essential, such as mobile platforms and embedded systems. The proposed method simplifies the model compression process, making it more accessible for practitioners who require fast deployment without enduring efficiency sacrifice.

On a theoretical level, the use of teacher models to both guide architecture search and facilitate knowledge transfer enriches current understanding of model compression. It challenges the necessity of separate entities for these tasks, suggesting an integrated framework that could redefine compression strategies for diverse neural network applications.

Prospects for Future AI Developments

The insights offered by this paper pave the way for further exploration into large-scale network compression without trade-offs on performance. Future advancements could refine these methods, potentially integrating more nuanced architectural variations and exploring the potential for automated hyperparameter adjustment within the one-step pruning process.

Conclusively, "Teachers Do More Than Teach" provides a robust framework for compressing GANs efficiently, presenting a compelling balance between computational economy and performance integrity. This methodology not only serves immediate needs in resource-constrained scenarios but also hints at a transformative trajectory for future AI model deployment and research paradigms.