Denoising Autoregressive Representation Learning

Exploring DARL: A Unified Model for Visual Representation and Generation

Introduction

In the pursuit of enhancing the capabilities of generative pre-training in computer vision, this paper introduces Denoising Autoregressive Representation Learning (DARL), a novel approach that marries the strengths of autoregressive and denoising diffusion models within a unified architecture. DARL employs a decoder-only Transformer tasked with predicting image patches autoregressively. Remarkably, this work demonstrates that performance closely aligns with state-of-the-art solutions, even under simple training regimes such as using Mean Squared Error (MSE) loss. Furthermore, by incorporating diffusion-based objectives, DARL subtly enhances its generative prowess, signaling an exciting progression toward versatile models capable of both sophisticated visual perception and generation.

Key Contributions

The paper makes several noteworthy contributions to the field of visual representation learning:

• Denoising Autoregressive Learning: DARL innovatively combines autoregressive prediction with denoising diffusion mechanisms. This hybrid approach enables robust visual representation learning, exhibiting near-parity with leading masked prediction models under fine-tuning evaluations.
• Positional Encoding Insights: Through extensive experimentation, the study underscores the efficacy of decomposed Rotary Positional Embedding (RoPE) for causal Transformers in visual tasks. This novel 2D RoPE outperforms traditional positional encoding schemes, particularly enhancing autoregressive models.
• Model Scaling and Noise Schedules: The research presents an exploration into the impacts of model size, training duration, and noise schedules on learning outcomes. Findings reveal that larger models and longer trainings, along with optimized noise schedules, favorably influence model performance.
• Efficacy of MSE and Diffusion Objectives: The study compares MSE loss against diffusion objectives for pre-training. Remarkably, MSE alone yields strong performance; however, diffusion objectives further refine generative capabilities, especially with tailored noise schedules and extended training regimes.

Theoretical and Practical Implications

From a theoretical standpoint, DARL's architecture prompts a reconsideration of generative pre-training's potential in visual tasks. It showcases that a unified model can adeptly handle both representation learning and image generation without compromising on performance. Practically, this work paves the way for more flexible and generalizable visual models that can be fine-tuned to a variety of downstream tasks with minimal performance loss, thereby broadening the applicability of generative models in real-world scenarios.

Future research could delve into refining the noise schedule and extending the model's capabilities to encompass more complex, multi-modal tasks. The insights regarding positional encoding also open avenues for further enhancing the Transformer architecture's applicability across various data types beyond images.

Conclusion

DARL marks a significant step towards realizing generative pre-training's full potential in vision. By adeptly blending autoregressive prediction with denoising diffusion processes within a cohesive framework, DARL not only matches but in some instances, surpasses the capabilities of contemporary benchmarks in visual representation learning. This research, by shedding light on the interactions between different model components and training objectives, contributes foundational knowledge that will undoubtedly inform the development of more advanced, versatile generative models in the future.