Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (1901.07261v3)

Abstract: Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propose an elastic search tactic at both micro and macro level, based on a hybrid controller that profits from evolutionary computation and reinforcement learning. Quantitative experiments help us to draw a conclusion that our generated models dominate most of the state-of-the-art methods with respect to the individual FLOPS.

• The paper introduces a NAS framework that automates the design of efficient SISR networks, balancing high restoration quality with low computational overhead.
• It leverages both micro- and macro-level search spaces to optimize cell operations and inter-block connections using evolutionary and reinforcement techniques.
• Evaluations on SET5 and Urban100 demonstrate superior PSNR/SSIM compared to handcrafted models, enabling real-time, resource-efficient deployment.

Essay on "Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search"

The paper "Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search" explores enhancing the efficiency and performance of single image super-resolution (SISR). It tackles the inherent challenges of balancing model complexity with restoration capacity—an aspect often complicated by the resource-intensive nature of deep convolutional networks traditionally used in SISR.

Core Contributions

The authors introduce a sophisticated approach utilizing Neural Architecture Search (NAS) to automate the design of efficient networks, optimizing them across multiple objectives. Specifically, they present an elastic search method that capitalizes on both micro-level (individual cell block) and macro-level (inter-block connectivity) search spaces. The use of a hybrid model generator, combining evolutionary algorithms with reinforcement learning, facilitates effective exploration and exploitation within this search space.

Methodology

The devised NAS framework strategically incorporates diverse cell blocks and complex interconnections to capture the nuances of SISR tasks effectively. This involves:

• Micro Search Space: Featuring operations, channel variations, kernel sizes, and residual connections, enhancing the architectural diversity of cell blocks.
• Macro Search Space: This ensures optimal connectivity between blocks, influencing information flow, and thus refining feature extraction and mapping processes.

The NAS pipeline encompasses initialization, selection, crossover, and mutation, emphasizing varied strategy applications to initialize model configurations and evolve these through generations.

Results and Evaluation

The authors evaluate their models on standard datasets, such as SET5 and Urban100, and report results that frequently surpass state-of-the-art handcrafted networks and comparable NAS-driven methods. Several models generated by their framework, such as FALSR-A, exhibit superior performance metrics in PSNR and SSIM, with substantially reduced computational overheads—reflective of efficient mult-add operations and minimized parameter usage.

Implications

This work has notable implications:

• Theoretical: The paper enriches the body of knowledge around NAS, particularly for SISR, indicating that dense connections, often held in high regard, might not always be optimal.
• Practical: It offers scalable solutions for deploying SISR models in resource-constrained environments, broadening the applicability of high-fidelity image restoration.

Future Directions

Potential future work might involve integrating advanced model evaluators, such as neural regressors, to further economize the time and resources expended in model training phases. Such advancements could significantly expedite the NAS process, enabling more rapid deployment of optimized models.

In summary, this paper presents an innovative framework that effectively reconciles power, speed, and resource efficiency in the field of super-resolution, setting a standard for future exploration and development in automated neural design strategies.