Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning

The intricacy of rainy image contents often leads cutting-edge deraining models to image degradation including remnant rain, wrongly-removed details, and distorted appearance. Such degradation is further exacerbated when applying the models trained on synthetic data to real-world rainy images. We observe two types of domain gaps between synthetic and real-world rainy images: one exists in rain streak patterns; the other is the pixel-level appearance of rain-free images. To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning. Semi-DRDNet consists of three sub-networks:i) for removing rain streaks without remnants, we present a squeeze-and-excitation based rain residual network; ii) for encouraging the lost details to return, we construct a structure detail context aggregation based detail repair network; to our knowledge, this is the first time; and iii) for building efficient contrastive constraints for both rain streaks and clean backgrounds, we exploit a novel dual sample-augmented contrastive regularization network.Semi-DRDNet operates smoothly on both synthetic and real-world rainy data in terms of deraining robustness and detail accuracy. Comparisons on four datasets including our established Real200 show clear improvements of Semi-DRDNet over fifteen state-of-the-art methods. Code and dataset are available at https://github.com/syy-whu/DRD-Net.Comment: 17 page

심층 신경망의 손실표면 및 딥러닝의 여러 적용에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 수리과학부, 2022. 8. 강명주.본 학위 논문은 심층 신경망의 손실 표면에 대하여 다룬다. 심층 신경망의 손실 함수는 볼록 함수와 같이 나쁜 국소점을 가지는가? 조각적으로 선형은 활성함수를 가지는 경우에 대해서는 잘 알려였지만, 일반적인 매끄러운 활성함수를 가지는 심층 신경망에 대해서는 아직까지 알려지지 않은 것이 많다. 본 연구에서는 나쁜 국소점이 일반적인 매끄러운 활성함수에서도 존재함을 보인다. 이것은 심층 신경망의 손실 표면에 대한 이해에 부분적인 설명을 제공해 줄 것이다. 추가적으로 본 논문에서는 학습 이론, 사생활 보호적인 기계 학습, 컴퓨터 비전 등의 분야에서의 심층 신경망의 다양한 응용을 선보일 예정이다.In this thesis, we study the loss surface of deep neural networks. Does the loss function of deep neural network have no bad local minimum like the convex function? Although it is well known for piece-wise linear activations, not much is known for the general smooth activations. We explore that a bad local minimum also exists for general smooth activations. In addition, we characterize the types of such local minima. This provides a partial explanation for the understanding of the loss surface of deep neural networks. Additionally, we present several applications of deep neural networks in learning theory, private machine learning, and computer vision.Abstract v 1 Introduction 1 2 Existence of local minimum in neural network 4 2.1 Introduction 4 2.2 Local Minima and Deep Neural Network 6 2.2.1 Notation and Model 6 2.2.2 Local Minima and Deep Linear Network 6 2.2.3 Local Minima and Deep Neural Network with piece-wise linear activations 8 2.2.4 Local Minima and Deep Neural Network with smooth activations 10 2.2.5 Local Valley and Deep Neural Network 11 2.3 Existence of local minimum for partially linear activations 12 2.4 Absence of local minimum in the shallow network for small N 17 2.5 Existence of local minimum in the shallow network 20 2.6 Local Minimum Embedding 36 3 Self-Knowledge Distillation via Dropout 40 3.1 Introduction 40 3.2 Related work 43 3.2.1 Knowledge Distillation 43 3.2.2 Self-Knowledge Distillation 44 3.2.3 Semi-supervised and Self-supervised Learning 44 3.3 Self Distillation via Dropout 45 3.3.1 Method Formulation 46 3.3.2 Collaboration with other method 47 3.3.3 Forward versus reverse KL-Divergence 48 3.4 Experiments 53 3.4.1 Implementation Details 53 3.4.2 Results 54 3.5 Conclusion 62 4 Membership inference attacks against object detection models 63 4.1 Introduction 63 4.2 Background and Related Work 65 4.2.1 Membership Inference Attack 65 4.2.2 Object Detection 66 4.2.3 Datasets 67 4.3 Attack Methodology 67 4.3.1 Motivation 69 4.3.2 Gradient Tree Boosting 69 4.3.3 Convolutional Neural Network Based Method 70 4.3.4 Transfer Attack 73 4.4 Defense 73 4.4.1 Dropout 73 4.4.2 Diff erentially Private Algorithm 74 4.5 Experiments 75 4.5.1 Target and Shadow Model Setup 75 4.5.2 Attack Model Setup 77 4.5.3 Experiment Results 78 4.5.4 Transfer Attacks 80 4.5.5 Defense 81 4.6 Conclusion 81 5 Single Image Deraining 82 5.1 Introduction 82 5.2 Related Work 86 5.3 Proposed Network 89 5.3.1 Multi-Level Connection 89 5.3.2 Wide Regional Non-Local Block 92 5.3.3 Discrete Wavelet Transform 94 5.3.4 Loss Function 94 5.4 Experiments 95 5.4.1 Datasets and Evaluation Metrics 95 5.4.2 Datasets and Experiment Details 96 5.4.3 Evaluations 97 5.4.4 Ablation Study 104 5.4.5 Applications for Other Tasks 107 5.4.6 Analysis on multi-level features 109 5.5 Conclusion 111 The bibliography 112 Abstract (in Korean) 129박

Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity

Image deraining is a typical low-level image restoration task, which aims at decomposing the rainy image into two distinguishable layers: the clean image layer and the rain layer. Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rains makes them less generalized to different real rainy scenes. Moreover, the existing methods mainly utilize the property of the two layers independently, while few of them have considered the mutually exclusive relationship between the two layers. In this work, we propose a novel non-local contrastive learning (NLCL) method for unsupervised image deraining. Consequently, we not only utilize the intrinsic self-similarity property within samples but also the mutually exclusive property between the two layers, so as to better differ the rain layer from the clean image. Specifically, the non-local self-similarity image layer patches as the positives are pulled together and similar rain layer patches as the negatives are pushed away. Thus the similar positive/negative samples that are close in the original space benefit us to enrich more discriminative representation. Apart from the self-similarity sampling strategy, we analyze how to choose an appropriate feature encoder in NLCL. Extensive experiments on different real rainy datasets demonstrate that the proposed method obtains state-of-the-art performance in real deraining.Comment: 10 pages, 10 figures, accept to 2022CVP

RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining

As a common weather, rain streaks adversely degrade the image quality. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability. In specific, we first establish a RCD model for representing rain streaks and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. By unfolding it, we then build the RCDNet in which every network module has clear physical meanings and corresponds to each operation involved in the algorithm. This good interpretability greatly facilitates an easy visualization and analysis on what happens inside the network and why it works well in inference process. Moreover, taking into account the domain gap issue in real scenarios, we further design a novel dynamic RCDNet, where the rain kernels can be dynamically inferred corresponding to input rainy images and then help shrink the space for rain layer estimation with few rain maps so as to ensure a fine generalization performance in the inconsistent scenarios of rain types between training and testing data. By end-to-end training such an interpretable network, all involved rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to better deraining performance. Comprehensive experiments substantiate the superiority of our method, especially on its well generality to diverse testing scenarios and good interpretability for all its modules. Code is available in \emph{\url{https://github.com/hongwang01/DRCDNet}}

Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity

Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rain makes them less generalized to complex real rainy scenes. Moreover, the existing methods mainly utilize the property of the image or rain layers independently, while few of them have considered their mutually exclusive relationship. To solve above dilemma, we explore the intrinsic intra-similarity within each layer and inter-exclusiveness between two layers and propose an unsupervised non-local contrastive learning (NLCL) deraining method. The non-local self-similarity image patches as the positives are tightly pulled together, rain patches as the negatives are remarkably pushed away, and vice versa. On one hand, the intrinsic self-similarity knowledge within positive/negative samples of each layer benefits us to discover more compact representation; on the other hand, the mutually exclusive property between the two layers enriches the discriminative decomposition. Thus, the internal self-similarity within each layer (similarity) and the external exclusive relationship of the two layers (dissimilarity) serving as a generic image prior jointly facilitate us to unsupervisedly differentiate the rain from clean image. We further discover that the intrinsic dimension of the non-local image patches is generally higher than that of the rain patches. This motivates us to design an asymmetric contrastive loss to precisely model the compactness discrepancy of the two layers for better discriminative decomposition. In addition, considering that the existing real rain datasets are of low quality, either small scale or downloaded from the internet, we collect a real large-scale dataset under various rainy kinds of weather that contains high-resolution rainy images.Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:2203.1150

Toward Real-world Single Image Deraining: A New Benchmark and Beyond

Single image deraining (SID) in real scenarios attracts increasing attention in recent years. Due to the difficulty in obtaining real-world rainy/clean image pairs, previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs, resulting in incomprehensive evaluation of SID methods. To address these issues, we establish a new high-quality dataset named RealRain-1k, consisting of $1,120$ high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively. Images in RealRain-1k are automatically generated from a large number of real-world rainy video clips through a simple yet effective rain density-controllable filtering method, and have good properties of high image resolution, background diversity, rain streaks variety, and strict spatial alignment. RealRain-1k also provides abundant rain streak layers as a byproduct, enabling us to build a large-scale synthetic dataset named SynRain-13k by pasting the rain streak layers on abundant natural images. Based on them and existing datasets, we benchmark more than 10 representative SID methods on three tracks: (1) fully supervised learning on RealRain-1k, (2) domain generalization to real datasets, and (3) syn-to-real transfer learning. The experimental results (1) show the difference of representative methods in image restoration performance and model complexity, (2) validate the significance of the proposed datasets for model generalization, and (3) provide useful insights on the superiority of learning from diverse domains and shed lights on the future research on real-world SID. The datasets will be released at https://github.com/hiker-lw/RealRain-1

Blind Image Decomposition

We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may consist of multiple components, such as rain streaks, raindrops, snow, and haze. Rainy images can be treated as an arbitrary combination of these components, some of them or all of them. How to decompose superimposed images, like rainy images, into distinct source components is a crucial step toward real-world vision systems. To facilitate research on this new task, we construct multiple benchmark datasets, including mixed image decomposition across multiple domains, real-scenario deraining, and joint shadow/reflection/watermark removal. Moreover, we propose a simple yet general Blind Image Decomposition Network (BIDeN) to serve as a strong baseline for future work. Experimental results demonstrate the tenability of our benchmarks and the effectiveness of BIDeN.Comment: ECCV 2022. Project page: https://junlinhan.github.io/projects/BID.html. Code: https://github.com/JunlinHan/BI