BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities (2410.14672v3)

Abstract: We introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generation and representation capabilities. BiGR is the first conditional generative model that unifies generation and discrimination within the same framework. BiGR features a binary tokenizer, a masked modeling mechanism, and a binary transcoder for binary code prediction. Additionally, we introduce a novel entropy-ordered sampling method to enable efficient image generation. Extensive experiments validate BiGR's superior performance in generation quality, as measured by FID-50k, and representation capabilities, as evidenced by linear-probe accuracy. Moreover, BiGR showcases zero-shot generalization across various vision tasks, enabling applications such as image inpainting, outpainting, editing, interpolation, and enrichment, without the need for structural modifications. Our findings suggest that BiGR unifies generative and discriminative tasks effectively, paving the way for further advancements in the field. We further enable BiGR to perform text-to-image generation, showcasing its potential for broader applications.

• The paper introduces BiGR, a model that unifies image generation and visual representation using compact binary latent codes.
• It employs a binary tokenizer, a decoder-only transformer with bidirectional attention, and a binary transcoder for efficient code processing.
• The entropy-ordered sampling method minimizes iterations, boosting both generation quality and zero-shot generalization performance.

Overview of "BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities"

This paper introduces BiGR, a conditional image generation model leveraging binary latent codes to enhance both generative and discriminative tasks. The authors propose a unified framework that exploits novel techniques aiming at improving the integration of image generation and visual representation capabilities.

Key Contributions

1. Binary Latent Code Utilization: BiGR is distinguished by its use of compact binary latent codes, which streamline both the generative process and representation learning. By employing these codes, BiGR integrates generation with discrimination seamlessly, an objective that has been elusive for previous generative models.
2. Framework Components: The model consists of a binary tokenizer, a decoder-only transformer with bidirectional attention, and a binary transcoder. Each of these components plays a critical role in the processing and transformation of binary latent codes, enhancing the model's performance across tasks.
3. Entropy-Ordered Sampling: The authors introduce an efficient sampling method based on entropy-ordered strategies. This process improves the image generation efficiency by minimizing the number of sampling iterations required, contrasting with existing models that often rely on sequential steps or extensive denoising.
4. Generative and Discriminative Performance: The research presents strong numerical results indicating superior performance in generation quality, as measured by FID-50k, and linear-probe accuracy benchmarks for representation tasks. This demonstrates BiGR's capability to excel in both domains, unlike models primarily optimized for one type of task.

Implications

The integration of binary latent codes within BiGR offers several implications:

• Efficiency: By unifying generative and discriminative tasks, BiGR promises improved computational efficiency. This could potentially reduce resource consumption in applications that require robust feature extraction alongside image generation.
• Flexibility and Scalability: The flexibility of BiGR in task-specific applications without structural modifications highlights its adaptability. Additionally, its scalability across both small and large models paves the way for diverse deployment scenarios, from lightweight applications to extensive systems.
• Zero-Shot Generalization: The capability of BiGR to perform zero-shot tasks such as image editing and enrichment further extends its utility, making it a versatile tool for numerous vision-related applications.

Future Developments

BiGR exemplifies a significant stride towards bridging the gap between generative and discriminative tasks in computer vision. Future developments may explore:

• Further Optimization: Enhancing the sampling strategies and binary transcoder prediction could push efficiency and performance boundaries even further.
• Extended Application: BiGR could be adapted for more complex vision tasks, including multi-modal processing or integration with natural language capabilities, broadening its application scope.
• Ethical and Responsible Use: As with any generative model, ensuring ethical usage while preventing harm is crucial. Developing safeguards and promoting awareness around potential misuse will be essential in guiding BiGR’s future deployments.

In conclusion, the paper provides a comprehensive framework that successfully harnesses binary latent codes for unified generative and discriminative tasks. It establishes a foundation for future exploration and refinement in the domain of conditional image generation and visual representation learning.