Generative Models: What Do They Know? Do They Know Things? Let's Find Out! (2311.17137v3)

Abstract: Generative models excel at mimicking real scenes, suggesting they might inherently encode important intrinsic scene properties. In this paper, we aim to explore the following key questions: (1) What intrinsic knowledge do generative models like GANs, Autoregressive models, and Diffusion models encode? (2) Can we establish a general framework to recover intrinsic representations from these models, regardless of their architecture or model type? (3) How minimal can the required learnable parameters and labeled data be to successfully recover this knowledge? (4) Is there a direct link between the quality of a generative model and the accuracy of the recovered scene intrinsics? Our findings indicate that a small Low-Rank Adaptators (LoRA) can recover intrinsic images-depth, normals, albedo and shading-across different generators (Autoregressive, GANs and Diffusion) while using the same decoder head that generates the image. As LoRA is lightweight, we introduce very few learnable parameters (as few as 0.04% of Stable Diffusion model weights for a rank of 2), and we find that as few as 250 labeled images are enough to generate intrinsic images with these LoRA modules. Finally, we also show a positive correlation between the generative model's quality and the accuracy of the recovered intrinsics through control experiments.

• The paper introduces a novel method using low-rank adaptation to convert generative models into accurate predictors of scene intrinsics with less than 0.6% extra parameters.
• It evaluates diverse architectures, including GANs, diffusion, and autoregressive models, and demonstrates competitive performance in predicting surface normals, depth, and shading.
• Implications extend to augmented reality, 3D modeling, and propose a new metric for generative model evaluation based on intrinsic prediction capabilities.

An Analysis of Intrinsic Capabilities in Generative Models via Low-Rank Adaptation

The research paper titled "Generative Models: What do they know? Do they know things? Let's find out!" presents a thorough investigation into the intrinsic capabilities of various generative models. The authors aim to elucidate whether large-scale generative models can implicitly learn critical scene intrinsics such as surface normals, depth, albedo, and shading. This essay provides an expert overview of the methodologies, findings, and implications of this paper, primarily targeting experienced researchers in the domain of computer vision and machine learning.

Introduction and Problem Statement

Generative models, including VQGAN, StyleGAN-v2, StyleGAN-XL, and Stable Diffusion, have demonstrated impressive capabilities in synthesizing realistic images. The paper seeks to understand if and how these models can also encode intrinsic properties of scenes without explicit supervision. Historically, understanding underlying scene properties from images is a significant challenge in computer vision, traditionally addressed via supervised learning. The presented work hypothesizes that generative models, which produce high-quality images, inherently capture these scene properties.

Methodology

The authors introduce a universal, model-agnostic approach leveraging Low-Rank Adaptation (LoRA) to transform any generative model into a scene intrinsic predictor. The approach is notable for its efficiency, introducing additional parameters that constitute less than 0.6% of the total model parameters. This minor addition enables the extraction of high-quality scene intrinsics directly from the generator network without fully fine-tuning it or adding new decoders. The paper extensively evaluates several types of generative models: Diffusion models, GANs, and Autoregressive models.

The core methodology involves fine-tuning low-rank matrices on top of key feature maps, such as attention layers in diffusion models and affine layers in StyleGANs. This adaptation is done using a small set of labeled images, facilitating a minimal change that preserves the original model's image generation capabilities.

Results and Analysis

The results showcase that the proposed adaptation method can successfully extract accurate scene intrinsics across various generative models. The method's effectiveness is evaluated both qualitatively and quantitatively. The extracted intrinsics such as surface normals, depth, albedo, and shading from the adapted models compare favorably with those produced by state-of-the-art supervised techniques.

Numerical Highlights

1. Surface Normals Extraction: The results indicate mean angular errors ranging between 13.24° and 24.09°, with the best performance observed in models trained on well-structured datasets like FFHQ.
2. Depth Extraction: Performance metrics for depth extraction showed RMS errors as low as 0.897, demonstrating the method’s efficacy.
3. Parameter Efficiency: The additional parameters introduced by LoRA are consistently below 0.6%, emphasizing the method's efficiency.

Implications and Future Directions

The implications of this paper are multi-fold. Practically, the ability to extract scene intrinsics from generative models without significant retraining opens up new avenues for applying these models to tasks such as augmented reality, 3D modeling, and advanced image analyses. Theoretically, the findings suggest that high-quality image generation and scene understanding are correlated, reinforcing the idea that generative objectives on large scales can inherently capture physical world properties.

Several interesting questions and future directions arise from this work:

1. Improving Generative Models: The paper hints at a possible metric for evaluating generative models based on their intrinsic predictive capabilities, offering an alternative to traditional metrics like FID.
2. Broadening Applications: Extending the proposed methodology to other types of generative models, including those yet to be developed, could further validate and expand the utility of this approach.
3. Incorporating Intrinsics in Training: Future research could explore explicitly incorporating scene intrinsic prediction into the training objectives of generative models, potentially improving both image generation and intrinsic prediction simultaneously.

Conclusion

This paper makes significant contributions to our understanding of the knowledge encoded by generative models. By introducing a low-cost, efficient approach to unlock scene intrinsic predictions, the research bridges a critical gap between generative image synthesis and scene understanding. The findings that even minimal adjustments using LoRA can reveal high-quality scene intrinsic maps challenge and refine our understanding of what generative models inherently learn. Further exploration and improvements in these models can lead to broader applications and more integrated approaches in artificial intelligence and computer vision domains.