Conditional Image Generation with PixelCNN Decoders
This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet, with greatly reduced computational cost.
Code (14)
Tasks
Conditional Image GenerationDecoderDensity EstimationDescriptiveImage GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Parallel Multiscale Autoregressive Density Estimation
PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by c…
Conditional Image GenerationDensity EstimationImage CompressionImage Generation+1Locally Masked Convolution for Autoregressive Models
High-dimensional generative models have many applications including image compression, multimedia generation, anomaly detection and data completion. State-of-the-art estimators for natural images are autoregressive, deco…
Anomaly DetectionDensity EstimationImage CompressionImage GenerationSpatial PixelCNN: Generating Images from Patches
In this paper we propose Spatial PixelCNN, a conditional autoregressive model that generates images from small patches. By conditioning on a grid of pixel coordinates and global features extracted from a Variational Auto…
The Variational Homoencoder: Learning to learn high capacity generative models from few examples
Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expr…
General ClassificationMultimodal Controller for Generative Models
Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nontrivial modifications of backbone generat…