Syn2Real Transfer Learning for Image Deraining using Gaussian Processes (2006.05580v1)

Abstract: Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data and hence, generalize poorly to real-world images. The use of real-world data in training image deraining networks is relatively less explored in the literature. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset while generalizing better using unlabeled real-world images. Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain200H and DDN-SIRR), we show that the proposed method, when trained on limited labeled data, achieves on-par performance with fully-labeled training. Additionally, we demonstrate that using unlabeled real-world images in the proposed GP-based framework results in superior performance as compared to existing methods. Code is available at: https://github.com/rajeevyasarla/Syn2Real

• The paper proposes a semi-supervised image deraining framework using Gaussian Processes to bridge the synthetic-to-real gap by leveraging unlabeled real-world data.
• The framework achieves performance comparable to fully supervised networks while requiring only a fraction of the labeled data.
• This method is practical for real-world applications like autonomous vehicles and other vision tasks with limited labeled data by reducing dependency on extensive labeling.

Syn2Real Transfer Learning for Image Deraining: A Gaussian Process Approach

The paper "Syn2Real Transfer Learning for Image Deraining using Gaussian Processes" addresses a pertinent challenge in computer vision: efficient image deraining. Given that rain streaks can significantly impede the performance of automated systems such as object detection algorithms, developing robust deraining algorithms is crucial.

The authors identify two critical issues with current deraining methodologies. Firstly, most methods perform admirably only when trained with fully labeled datasets, which are difficult to acquire in real-world settings due to the inherent challenge of obtaining paired rainy and clean images. Consequently, these methods predominantly rely on synthetically generated data, which often results in poor generalization to real-world scenarios. The second issue relates to the assumption about the nature of rain streaks, which vary in scale, density, and orientation.

In addressing these issues, the paper proposes a novel semi-supervised learning framework utilizing Gaussian processes (GP), which has the potential to bridge the gap between synthetic training data and real-world applications. This approach circumvents the dependency on large labeled datasets while enhancing generalization capabilities by incorporating unlabeled real-world data into the training process.

The framework's novelty lies in its use of GPs to model and supervise the latent features during network training. The proposed method employs a data-driven strategy where a convolutional neural network projects input features onto a latent space. During training, the network learns from labeled synthetic pairs and concurrently adapts to real-world data by generating pseudo-ground-truth for unlabeled samples via GP. This dual-phase process empowers the network to perform on par with fully supervised networks but only requires a fraction of labeled data.

The experimental evaluations on datasets—Rain800, Rain200H, and DDN-SIRR—demonstrate that even with minimal labeled data, the proposed GP-based framework achieves significant performance improvements, as evidenced by metrics like PSNR and SSIM. For example, the paper reports a gain of up to 2.12 PSNR points in certain setups, underscoring the efficacy of using unlabeled data to potentiate network generalization.

Unlike previous works like SIRR, which leverages Gaussian Mixture Models, this framework does not depend on parametrizing distributions with a predefined number of components, thus avoiding the pitfalls associated with erroneous initial parameter estimation. The GP also serves to dynamically adapt the model to variations present in unlabeled datasets, thus reducing variance and bolstering robustness.

In conclusion, this paper contributes a sophisticated method for enhancing image deraining mechanisms, particularly by harnessing unlabeled real-world data—a direction that paves the way for future research focused on reducing the dependency on synthetic data. It points toward interesting future directions, such as expanding this semi-supervised approach to other domains within computer vision where labeled data is rare, yet variability in real-world conditions prevails. The potential for deploying such methodologies in real-time systems like autonomous vehicles or surveillance systems could hence be vastly augmented.