Conditional Image Generation with PixelCNN Decoders (1606.05328v2)

Abstract: 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.

• The paper presents a gated PixelCNN that mitigates blind spots and reduces computational overhead while achieving competitive NLL scores on CIFAR-10 and ImageNet.
• It leverages high-level conditioning vectors, including class labels and portrait embeddings, to generate diverse and realistic images.
• Integrating the PixelCNN decoder into autoencoder frameworks improves high-dimensional data reconstruction and representation learning.

Conditional Image Generation with PixelCNN Decoders

In the paper Conditional Image Generation with PixelCNN Decoders, the authors address various aspects of image generation by focusing on a convolutional variant of the PixelRNN architecture. Central to this work is the Conditional PixelCNN, a model designed to handle the intricate task of generating images conditioned on high-level descriptive vectors. These vectors can include class labels, latent embeddings from other networks, or other descriptive tags.

Highlights of the Research

1. Gated PixelCNN Architecture: The Gated PixelCNN introduces gated activation units to PixelCNN, enhancing its ability to model complex dependencies. By incorporating a second convolutional stack, the authors alleviate the "blind spot" issue inherent in the original PixelCNN architecture. This architectural enhancement successfully matches the performance of PixelRNN while significantly reducing computational overhead.
2. Class-Conditional Generation: The authors demonstrate the capability of Gated PixelCNN in generating images conditioned on ImageNet class labels. Their results show that it can produce diverse and realistic images across various classes. The model does not simply replicate class-specific features but generates variations in poses, angles, and lighting conditions, suggesting a robust internal representation of each class.
3. Portrait Embeddings: Another compelling application of Conditional PixelCNN is its use in generating new portraits based on embeddings from a convolutional network trained on a large dataset of faces. When conditioned on an embedding derived from a single image, the model generates portraits of the same individual with different expressions and lighting, highlighting the model's capacity to handle high-level conditioning information efficiently.
4. PixelCNN Autoencoders: Exploring a novel application, the authors incorporate Conditional PixelCNN as the decoder in an autoencoder framework. The resulting autoencoder demonstrates superior performance in reconstructing high-dimension data as compared to traditional convolutional autoencoders. The influence of the PixelCNN decoder steers the encoder to capture high-level abstract information efficiently, evident from the diverse reconstructions even from compressed latent spaces.

Experimental Results

Empirical evidence from the experiments substantiates the efficacy of Gated PixelCNN. On the CIFAR-10 dataset, Gated PixelCNN achieves a negative log-likelihood (NLL) of 3.03 bits/dim, surpassing the PixelCNN's 3.14 and approaching PixelRNN's performance. On the ImageNet 32x32 dataset, Gated PixelCNN attains an NLL of 3.83 bits/dim, slightly outperforming PixelRNN (3.86). The most notable advantage is the reduced training time, significantly lowering computational costs.

Conditional experiments on ImageNet reinforced the robustness of Conditional PixelCNN. Despite marginal improvements in NLL scores, the models excelled qualitatively—with visually appealing samples across diverse categories and variations. Similarly, for portrait generation and autoencoders, the conditional PixelCNN models yielded high-quality representations and multi-sample generations, surpassing conventional models.

Practical and Theoretical Implications

The work has profound implications:

1. Computational Efficiency: By improving training efficiency while maintaining high performance, Gated PixelCNN positions itself as a practical choice for application domains requiring swift model training and inference, including real-time image generation in applications such as reinforcement learning and video frame prediction.
2. Robust Generative Modeling: The Conditional PixelCNN's ability to generate high-fidelity images conditioned on diverse embeddings opens avenues in personalized content creation, image editing tools, and data augmentation for limited datasets in healthcare or other fields.
3. Enhancements in Autoencoders: Integrating PixelCNN decoders in autoencoder frameworks can significantly enhance the quality of learned representations, facilitating improved performance in downstream tasks like anomaly detection, image retrieval, and feature extraction.

Future Directions

Anticipated future developments based upon this work may include:

• One-shot Learning Methods:

  Leveraging Conditional PixelCNNs for generating images from limited examples, potentially integrating with recent advances in meta-learning frameworks.

• Improvement in Variational Inference:

  Combining Conditional PixelCNNs with variational inference to refine decoders in variational autoencoders (VAEs) can enhance the richness of generated samples over the current Gaussian-based approaches.

• Cross-modal Generation:

  Extensions to model images based on natural language text descriptions or cross-modal embeddings might yield improved image captioning and multimodal translation models.

Overall, the paper distinctly illustrates significant strides in conditional generative modeling and injects considerable optimism into the capabilities and applications of image generation models.