Deep Learning on Image Denoising: An overview <s> Previous layer <s> In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations. <s> BIB001 </s> Deep Learning on Image Denoising: An overview <s> Previous layer <s> Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. <s> BIB002 </s> Deep Learning on Image Denoising: An overview <s> Previous layer <s> The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art. <s> BIB003 </s> Deep Learning on Image Denoising: An overview <s> Previous layer <s> In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem due to the lack of paired training data. To tackle the barrier, we propose a novel two-step framework. First, a Generative Adversarial Network (GAN) is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Second, the noise patches sampled from the first step are utilized to construct a paired training dataset, which is used, in turn, to train a deep Convolutional Neural Network (CNN) for denoising. Extensive experiments have been done to demonstrate the superiority of our approach in image blind denoising. <s> BIB004
Filter concatenation Although VGG and GoogLeNet methods are effective for image applications, they are faced with the following drawbacks: (1) if network is very deep, this network may result in vanishing or exploding gradients. (2) If network is very wide, it may encounter overfitting phenomenon. For overcoming these problems, ResNet BIB002 was proposed in 2016. That is, each block was added residual learning operation in the ResNet to improve the performance of image recognition, which also won ImageNet LSVR 2015. Here we use Fig. 5 to visually show the idea of the residual learning. Since 2014, deep networks have been widely used in the fields of image application in real world. However, captured images of many applications, such as real noisy images are not enough, deep CNNs have poor performance of image applications. Generative Adversarial Networks (GAN) BIB001 was developed based on this reason. The GAN had two networks: generative and discriminative networks. The generative network (also referred to as generator) is used to generate samples, according to input samples. The other network (also as well as discriminator) is used to judge truth of both input samples and generated samples. Two networks are adversarial. It is noted that if the discriminator can accurately distinguish real samples and generate samples from generator, the trained model is regarded to finishing. The network architecture of the GAN can be seen in Fig. 6 . Due to the strong ability of constructing supplement training samples, the GAN is very effective for small sample tasks, such as face recognition BIB003 and complex noisy image denoising BIB004 .