Inpaint key generator
The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. Applications of this technique include the restoration of old photographs and damaged film removal of superimposed text like dates, subtitles, or publicity and the removal of entire objects from the image like microphones or wires in special effects. In addition, no limitations are imposed on the topology of the region to be inpainted. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from.
The fill-in is done in such a way that isophote lines arriving at the regions' boundaries are completed inside. After the user selects the regions to be restored, the algorithm automatically fills-in these regions with information surrounding them.
#INPAINT KEY GENERATOR PROFESSIONAL#
In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. Finally, we propose additional intuitive constraints on the synthesis process that offer the user a level of control unavailable in previous methods. This one simple algorithm forms the basis for a variety of tools - image retargeting, completion and reshuffling - that can be used together in the context of a high-level image editing application. We offer theoretical analysis of the convergence properties of the algorithm, as well as empirical and practical evidence for its high quality and performance.
#INPAINT KEY GENERATOR PATCH#
The key insights driving the algorithm are that some good patch matches can be found via random sampling, and that natural coherence in the imagery allows us to propagate such matches quickly to surrounding areas. Our algorithm offers substantial performance improvements over the previous state of the art (20-100x), enabling its use in interactive editing tools. However, the cost of computing a field of such matches for an entire image has eluded previous efforts to provide interactive performance. Previous research in graphics and vision has leveraged such nearest-neighbor searches to provide a variety of high-level digital image editing tools. This paper presents interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest-neighbor matches between image patches. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark. For the evaluation of the performance of GANs at image generation, we introduce the `Fréchet Inception Distance'' (FID) which captures the similarity of generated images to real ones better than the Inception Score. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. TTUR has an individual learning rate for both the discriminator and the generator.
#INPAINT KEY GENERATOR UPDATE#
We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. However, the convergence of GAN training has still not been proved. Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.