FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search (2006.12986v2)

Abstract: Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, performance gains can be achieved by designing network architectures specifically for detection and segmentation, as shown by recent neural architecture search (NAS) research for detection and segmentation. One major challenge though is that ImageNet pre-training of the search space representation (a.k.a. super network) or the searched networks incurs huge computational cost. In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e.g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently. In our experiments, we apply FNA++ on MobileNetV2 to obtain new networks for semantic segmentation, object detection, and human pose estimation that clearly outperform existing networks designed both manually and by NAS. We also implement FNA++ on ResNets and NAS networks, which demonstrates a great generalization ability. The total computation cost of FNA++ is significantly less than SOTA segmentation and detection NAS approaches: 1737x less than DPC, 6.8x less than Auto-DeepLab, and 8.0x less than DetNAS. A series of ablation studies are performed to demonstrate the effectiveness, and detailed analysis is provided for more insights into the working mechanism. Codes are available at https://github.com/JaminFong/FNA.

• The paper introduces parameter remapping to transfer pre-trained network parameters across tasks without full retraining.
• It leverages neural architecture search to customize network structures for specific computer vision tasks while reducing computational cost.
• Empirical results demonstrate improved performance and resource efficiency on benchmarks like Cityscapes and MS-COCO compared to previous NAS techniques.

An Analysis of "FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search"

The paper presents FNA++, a method designed for the efficient adaptation of deep neural networks to new tasks. It addresses the inefficiencies of directly using architectures primarily tailored and pre-trained for image classification (like ImageNet) as backbones for other tasks such as object detection and semantic segmentation. The cornerstone of this approach is a novel parameter remapping paradigm aiding both architecture and parameter adaptation.

Key Contributions and Methodology

• Parameter Remapping: The authors introduce an innovative remapping strategy that allows transferring network parameters on different levels: depth, width, and kernel size. This does not demand extensive retraining, making it particularly useful when computational resources are limited. The method remaps parameters from a seed network to a super network, which then undergoes neural architecture search (NAS) for task-specific optimization.
• Fast Network Adaptation: FNA++ can efficiently adapt network architectures for specific computer vision tasks (e.g., semantic segmentation, object detection, and human pose estimation). The method is carried out in three primary steps: network expansion, architecture adaptation using a NAS method, and parameter adaptation, where the parameters are remapped following the adaptation of architecture.
• Computational Efficiency: Through comparative experiments, FNA++ demonstrates a significantly reduced computational cost compared to state-of-the-art NAS approaches, such as DPC and DetNAS, without sacrificing performance. This efficiency is achieved by leveraging pre-existing networks already trained on large datasets like ImageNet, obviating the need for complete retraining.

Results and Implications

The empirical results show that FNA++ performs well across various tasks, surpassing existing methods both manually designed and optimized through NAS. For instance, experiments in semantic segmentation on the Cityscapes dataset show up to 1.5% improvement in mean Intersection over Union (mIOU) with lower MAdds, while object detection on MS-COCO shows gains in mean Average Precision (mAP) with less resource usage compared to DetNAS.

The reduced computational burden introduced by FNA++ suggests significant implications for real-world applications, particularly in scenarios where resource constraints are a major concern. By lowering the overheads of adapting networks to task-specific architectures, FNA++ opens up new possibilities for deploying state-of-the-art models in embedded systems and mobile devices.

Conclusion and Future Directions

FNA++ presents a compelling solution to the challenge of adapting pre-trained networks to various vision tasks efficiently. While the work focuses largely on MobileNetV2 and ResNets, its generalization to other architectures highlights its flexibility. Future developments could involve further exploration of parameter remapping strategies, as well as detailed investigations into the balance between architecture complexity and task-specific performance. As the AI field progresses, leveraging such methods to streamline the deployment of models across different applications could provide substantial benefits in terms of both cost and speed.