Image De-Raining Using a Conditional Generative Adversarial Network
Severe weather conditions, such as rain and snow, adversely affect the visual quality of images captured under such conditions, thus rendering them useless for further usage and sharing. In addition, such degraded images drastically affect the performance of vision systems. Hence, it is important to address the problem of single image de-raining. However, the inherent ill-posed nature of the problem presents several challenges. We attempt to leverage powerful generative modeling capabilities of the recently introduced conditional generative adversarial networks (CGAN) by enforcing an additional constraint that the de-rained image must be indistinguishable from its corresponding ground truth clean image. The adversarial loss from GAN provides additional regularization and helps to achieve superior results. In addition to presenting a new approach to de-rain images, we introduce a new refined loss function and architectural novelties in the generator–discriminator pair for achieving improved results. The loss function is aimed at reducing artifacts introduced by GANs and ensure better visual quality. The generator sub-network is constructed using the recently introduced densely connected networks, whereas the discriminator is designed to leverage global and local information to decide if an image is real/fake. Based on this, we propose a novel single image de-raining method called image de-raining conditional generative adversarial network (ID-CGAN) that considers quantitative, visual, and also discriminative performance into the objective function. The experiments evaluated on synthetic and real images show that the proposed method outperforms many recent state-of-the-art single image de-raining methods in terms of quantitative and visual performances. Furthermore, the experimental results evaluated on object detection datasets using the Faster-RCNN also demonstrate the effectiveness of proposed method in improving the detection performance on images degraded by rain.
Face aging with conditional generative adversarial networks
It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the first GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person's identity in the aged version of his/her face. To this end, we introduce a novel approach for “Identity-Preserving” optimization of GAN's latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.
neural network machine learning artificial neural network deep learning convolutional neural network convolutional neural natural language deep neural network speech recognition social media neural network model hidden markov model markov model deep neural medical image computer vision object detection image classification conceptual design generative adversarial network gaussian mixture model facial expression generative adversarial deep convolutional neural deep reinforcement learning network architecture adversarial network mutual information deep learning model speech recognition system deep convolutional cad system image denoising speech enhancement neural network architecture convolutional network facial expression recognition feedforward neural network expression recognition nash equilibrium domain adaptation single image loss function based on deep neural net deep learning method semi-supervised learning deep learning algorithm data augmentation neural networks based image super-resolution deep belief network deep network feature learning enhancement based image synthesi multilayer neural network unsupervised domain adaptation learning task latent space single image super-resolution conditional generative adversarial media service neural networks trained acoustic modeling theoretic analysi speech enhancement based conditional generative multi-layer neural network quantitative structure-activity relationship conversational speech information bottleneck generative adversarial net training deep neural noisy label training deep adversarial perturbation adversarial net generative network batch normalization convolutional generative adversarial social media service deep convolutional generative update rule adversarial neural network deep neural net sensing mri convolutional generative adversarial sample wasserstein gan machine-learning algorithm robust training ventral stream binary weight gan training train deep neural ventral visual pathway deep generative adversarial current speech recognition pre-trained deep neural analysi of tweets deep feedforward neural improving deep learning frechet inception distance training generative adversarial stimulus feature medical image synthesi training generative community intelligence acoustic input overcoming catastrophic forgetting social reporting networks reveal context-dependent deep neural deep compression ventral pathway weights and activation extremely noisy