We show the combined results in Figure 1.

Recently, AutoRegressive (AR) models for the whole image generation empowered by transformers have achieved comparable or even better performance compared to Generative Adversarial Networks (GANs). Unfortunately, directly applying such AR models to edit/change local image regions, may suffer from the problems of missing global information, slow inference speed, and information leakage of local guidance. To address these limitations, we propose a novel model - image Local Autoregressive Transformer (iLAT), to better facilitate the locally guided image synthesis. Our iLAT learns the novel local discrete representations, by the newly proposed local autoregressive (LA) transformer of the attention mask and convolution mechanism. Thus iLAT can efficiently synthesize the local image regions by key guidance information. Our iLAT is evaluated on various locally guided image syntheses, such as pose-guided person image synthesis and face editing. Both quantitative and qualitative results show the efficacy of our model.

We study the regularisation induced in neural networks by Gaussian noise injections (GNIs). Though such injections have been extensively studied when applied to data, there have been few studies on understanding the regularising effect they induce when applied to network activations. Here we derive the explicit regulariser of GNIs, obtained by marginalising out the injected noise, and show that it penalises functions with high-frequency components in the Fourier domain; particularly in layers closer to a neural network's output. We show analytically and empirically that such regularisation produces calibrated classifiers with large classification margins.

In the supplementary material, we describe the training details, examples of game interface and interactions used in the paper. We train our model using the Advantage Actor Critic (A2C) method [37] across valid actions. Function to obtain the valid action set is provided by Jericho [20]. Similar to KG-A2C [3], a supervised auxiliary task "valid action prediction" is introduced to assist RL training. You are in attendance at the annual Grue Convention, this year a rather somber affair due to the "adventurer famine" that has gripped gruedom in this isolated corner of the empire.

A.1 Algorithmic details Algorithm 2 supports Algorithm 1 by demonstrating how the class prediction and expectations are calculated. Of note are two minor changes from prior implementations of this certification regime. The first is the addition of the Gumbel-Softmax on line 4, although this step is only required for the'Full' derivative approach. In contrast th'Approximate' techniques able to circumvent this limitation and can be applied directly to the case where the class election is determined by an arg max. Our initial testing revealed that when we employed either Sison-Glaz [38] or Goodman et al. [14] to estimate the multivariate class uncertainties, some Tiny-Imagenet samples devoted more than 95% of their computational time of the process to evaluating the confidence intervals, significantly outweighing even the costly process of model sampling.

In response to subtle adversarial examples flipping classifications of neural network models, recent research has promoted certified robustness as a solution. There, invariance of predictions to all norm-bounded attacks is achieved through randomised smoothing of network inputs. Today's state-of-the-art certifications make optimal use of the class output scores at the input instance under test: no better radius of certification (under the L

Since DSLR and Webcam do not have many examples, we conduct experiments on D to A, W to A, A to C (Caltech), D to C, and W to C shifts. The setting is the same as (11). The second benchmark dataset is OfficeHome (OH) (12), which contains four domains and 65 classes. The third dataset is VisDA (9), which contains 12 classes from the two domains, synthetic and real images. The synthetic domain consists of 152,397 synthetic 2D renderings of 3D objects and the real domain consists of 55,388 real images.

If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?

Approximate Bayesian inference estimates descriptors of an intractable target distribution - in essence, an optimization problem within a family of distributions.

In this supplementary material, we present more few-shot generation results evaluated extensively with different artistic domains where there are only a few examples available in practical. The goal is to illustrate the effectiveness of the proposed method in generating diverse high-quality results without being over-fitted to the few given examples. Figure 1 shows the generations of source and target domain by feeding the same latent code into the source and adapted model. It clearly tells that while the adaptation renders new appearance of target domain, other attributes such as the pose, glass and hairstyle, are well inherited and preserved from the source domain. For each target domain, we only use 10 examples for the adaptation and present 100 new results.