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In recent years, research on image generation has been developing very fast. The generative adversarial network (GAN) emerges as a promising framework, which uses adversarial training to improve the generative ability of its generator. However, since GAN and most of its variants use randomly sampled noises as the input of their generators, they have to learn a mapping function from a whole random distribution to the image manifold. As the structures of the random distribution and the image manifold are generally different, this results in GAN and its variants difficult to train and converge. In this paper, we propose a novel deep model called generative adversarial networks with decoder-encoder output noises (DE-GANs), which take advantage of both the adversarial training and the variational Bayesian inference to improve GAN and its variants on image generation performances. DE-GANs use a pre-trained decoder-encoder architecture to map the random noise vectors to informative ones and feed them to the generator of the adversarial networks. Since the decoder-encoder architecture is trained with the same data set as the generator, its output vectors, as the inputs of the generator, could carry the intrinsic distribution information of the training images, which greatly improves the learnability of the generator and the quality of the generated images. Extensive experiments demonstrate the effectiveness of the proposed model, DE-GANs.

AlphaGo trains a value network to predict the win rate of the current state with 7.5 komi on a 19 × 19 board. The komi of most rectangular boards is unknown, so we do not know who the winner is at the end of the game. We need to use the human experience to guess a komi and then train the value network with this komi. Therefore, the accuracy of the value network is related to the accuracy of the guess. This article uses the board value network to calculate the score of the current state and tries to maximize the score. Then, we do not need to guess the komi. We also modify the network structure to support the board with arbitrary board size as input. Furthermore, we can transfer knowledge of the small board to the large board. We propose an algorithm that can adapt to the bonus rule. We have experimentally proved that our method is effective on a small board and has the ability to transfer knowledge to the large board. In order to better understand the learning process, we visualize the policy and score of some major branches. Finally, we show the solution that our program obtained on 6 × 6, 6 × 7, and 7 × 8 boards.

Rain streaks will degrade the visibility of images. To tackle this problem, we propose a novel Adaptive Dilated Network (ADN) to remove rain streaks from a single image while using less parameters and running faster than previous methods. Specifically, an Adaptive Dilated Block (ADB) is constructed as the sub-module of ADN. In ADB, we apply a shared dilated block to extract multi-scale features. Then a dilated selection block is added to leverage the importance of features in different scales. All the multi-scale features are fused together to obtain features with rich rain details. To further model the inter-dependencies of the fused features, a feature selection block is employed in ADB to assign different weights to each feature. Moreover, all the hierarchical features extracted by each ADB are concatenated together and fed into a rainy map generator to estimate rain layer. Experimental results demonstrate that the proposed method is superior to the state-of-the-art methods on performances and running time while using less parameters. The source code is available at https://github.com/nnUyi/ADN.

Rainy images contain rain streaks with different sizes, shapes, directions, and densities. To efficiently remove rain streaks from rainy images, it is necessary to capture rich rain details. In this paper, we propose a Residual Dense Dilated Aggregated Network (RDDAN) to focus on different types of rain steaks and efficiently model rain distribution from rainy images. Specifically, a Residual Dense Dilated Aggregated Block (RDDAB) is constructed to fully extract and exploit rain details hierarchically. In RDDAB, dilated aggregated module is applied to capture multi-scale rain details, dense connection is employed to fully exploit hierarchical features extracted by dilated aggregated module, and residual connection is introduced to keep flow of rain details among different blocks. Besides, all the features extracted by each RDDAB are fused progressively which allows the network to adaptively focus on significant hierarchical features inter blocks. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on synthetic and real-world datasets. The source code is available at https://github.com/nnUyi/RDDAN.

Single image deraining is an important problem in many computer vision tasks since rain streaks can severely hamper and degrade the visibility of images. In this paper, we propose a novel network named Recurrent Hierarchy Enhancement Network (ReHEN) to remove rain streaks from rainy images stage by stage. Unlike previous deep convolutional network methods, we adopt a Hierarchy Enhancement Unit (HEU) to fully extract local hierarchical features and generate effective features. Then a Recurrent Enhancement Unit (REU) is added to keep the useful information from HEU and benefit the rain removal in the later stages. To focus on different scales, shapes, and densities of rain streaks adaptively, Squeeze-and-Excitation (SE) block is applied in both HEU and REU to assign different scale factors to high-level features. Experiments on five synthetic datasets and a real-world rainy image set show that the proposed method outperforms the state-of-the-art methods considerably. The source code is available at https://github.com/nnUyi/ReHEN.