Next, we would like to prove the opposite direction. All LeakyReLU activations have a slope of 0.02 for negative values except when we use a classic discriminator for single image training, for which we use a slope of 0.2. Additionally, the generator's last conv-block activation at each scale is Tanh instead of ReLU and the discriminator's last We clip the gradient s.t it has a maximal L2 norm of 1 for both the generators and Batch sizes of 16 were used for all experiments involving a dataset of images. At test time, the GPU memory usage is significantly reduced and requires 5GB. In this section, we consider training our method with a "frozen" pretrained ResNet34 i.e., optimizing If the problem could be learned with a "small enough" depth, our method would benefit from even As can be seen, our method yields realistic results with any batch size.

Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively.In the presented meta-learning approach, a single-image GAN model is generated given an input image, via a convolutional feedforward hypernetwork $f$. This network is trained over a dataset of images, allowing for feature sharing among different models, and for interpolation in the space of generative models. The generated single-image model contains a hierarchy of multiple generators and discriminators. It is therefore required to train the meta-learner in an adversarial manner, which requires careful design choices that we justify by a theoretical analysis. Our results show that the models obtained are as suitable as single-image GANs for many common image applications, {significantly reduce the training time per image without loss in performance}, and introduce novel capabilities, such as interpolation and feedforward modeling of novel images.

Next, we would like to prove the opposite direction. All LeakyReLU activations have a slope of 0.02 for negative values except when we use a classic discriminator for single image training, for which we use a slope of 0.2. Additionally, the generator's last conv-block activation at each scale is Tanh instead of ReLU and the discriminator's last We clip the gradient s.t it has a maximal L2 norm of 1 for both the generators and Batch sizes of 16 were used for all experiments involving a dataset of images. At test time, the GPU memory usage is significantly reduced and requires 5GB. In this section, we consider training our method with a "frozen" pretrained ResNet34 i.e., optimizing If the problem could be learned with a "small enough" depth, our method would benefit from even As can be seen, our method yields realistic results with any batch size.

Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively.In the presented meta-learning approach, a single-image GAN model is generated given an input image, via a convolutional feedforward hypernetwork f . This network is trained over a dataset of images, allowing for feature sharing among different models, and for interpolation in the space of generative models. The generated single-image model contains a hierarchy of multiple generators and discriminators.