Interpreting Super-Resolution Networks with Local Attribution Maps (2011.11036v2)

Abstract: Image super-resolution (SR) techniques have been developing rapidly, benefiting from the invention of deep networks and its successive breakthroughs. However, it is acknowledged that deep learning and deep neural networks are difficult to interpret. SR networks inherit this mysterious nature and little works make attempt to understand them. In this paper, we perform attribution analysis of SR networks, which aims at finding the input pixels that strongly influence the SR results. We propose a novel attribution approach called local attribution map (LAM), which inherits the integral gradient method yet with two unique features. One is to use the blurred image as the baseline input, and the other is to adopt the progressive blurring function as the path function. Based on LAM, we show that: (1) SR networks with a wider range of involved input pixels could achieve better performance. (2) Attention networks and non-local networks extract features from a wider range of input pixels. (3) Comparing with the range that actually contributes, the receptive field is large enough for most deep networks. (4) For SR networks, textures with regular stripes or grids are more likely to be noticed, while complex semantics are difficult to utilize. Our work opens new directions for designing SR networks and interpreting low-level vision deep models.

• The paper introduces Local Attribution Maps using blurred baselines and progressive blurring to clarify key input features in super-resolution networks.
• It employs an enhanced integral gradient method that ensures smoother transitions and robust visualization of local contributions.
• The analysis demonstrates that broader input pixel utilization and effective receptive field strategies can significantly improve SR network performance.

Overview of "Interpreting Super-Resolution Networks with Local Attribution Maps"

The paper "Interpreting Super-Resolution Networks with Local Attribution Maps" consolidates an approach for understanding image super-resolution (SR) networks. It emphasizes the interpretability of these networks, which has been a challenging aspect in the field of deep learning due to their complexity and abstract nature. Through the proposition of Local Attribution Maps (LAM), the authors aim to highlight the importance of input features that critically affect the output in SR networks.

Methodology

The authors employ a novel local attribution approach leaning on the integral gradient method, enhanced by two distinctive features:

1. Baseline Input: The use of blurred images as baseline inputs instead of the traditional zero-input or black images enhances the representation of absent features effectively.
2. Path Function: Introduction of the progressive blurring function as a path function for smoother transition between baseline and actual inputs, offering a robust alternative to linear interpolation.

These innovations facilitate the analysis and visualization of feature importance in SR networks, specifically focusing on local patches instead of the global image.

Key Findings

The application of LAM in SR networks brings forth several insights:

• Wide Range of Input Pixels: Networks that leverage a broader scope of input pixels tend to achieve superior performance, signifying the potential benefits of network deepening or widening.
• Attention and Non-Local Networks: These network architectures extract features from a wider range of pixels effectively, suggesting their design suitability in improving SR outcomes.
• Receptive Field vs. Effective Range: There exists a discrepancy between the receptive field size and the actual influential pixel range; increasing the receptive field alone may not enhance pixel utilization.
• Texture and Semantic Utilization: Existing SR networks are more inclined to utilize textures with regular patterns, while complex semantics remain underutilized.

These observations offer substantial implications for designing and interpreting SR networks, pushing boundaries toward more effective utilization of information.

Implications

The insights derived from LAM can guide the improvement of SR network architectures:

• Emphasizing wider and deeper network designs could unlock higher performance benchmarks.
• Prioritizing mechanisms that efficiently broaden receptive fields while ensuring effective pixel utilization can enhance feature extraction capabilities.
• Addressing challenges in leveraging complex semantics over simple textures in SR processes can propel advancements in network interpretability.

Future Directions

Future research can proceed by exploring these outlined avenues, such as developing methods for better semantic utilization in SR processes, optimizing network designs to reconcile receptive field size with effective pixel range, and employing LAM in other domains of deep learning and computer vision. There is potential for LAM-based strategies to profoundly impact low-level vision tasks beyond SR networks.

In conclusion, the paper provides a comprehensive methodological framework for interpreting super-resolution networks with practical and theoretical improvements, contributing valuably to the existing literature and paving paths for forward-looking developments in AI.