Denoising Diffusion Autoencoders are Unified Self-supervised Learners (2303.09769v2)

Abstract: Inspired by recent advances in diffusion models, which are reminiscent of denoising autoencoders, we investigate whether they can acquire discriminative representations for classification via generative pre-training. This paper shows that the networks in diffusion models, namely denoising diffusion autoencoders (DDAE), are unified self-supervised learners: by pre-training on unconditional image generation, DDAE has already learned strongly linear-separable representations within its intermediate layers without auxiliary encoders, thus making diffusion pre-training emerge as a general approach for generative-and-discriminative dual learning. To validate this, we conduct linear probe and fine-tuning evaluations. Our diffusion-based approach achieves 95.9% and 50.0% linear evaluation accuracies on CIFAR-10 and Tiny-ImageNet, respectively, and is comparable to contrastive learning and masked autoencoders for the first time. Transfer learning from ImageNet also confirms the suitability of DDAE for Vision Transformers, suggesting the potential to scale DDAEs as unified foundation models. Code is available at github.com/FutureXiang/ddae.

• The paper demonstrates that DDAEs can merge generative synthesis and discriminative feature extraction, achieving 95.9% accuracy on CIFAR-10.
• It introduces a unified self-supervised framework that leverages multi-level Gaussian noise perturbations to produce linear-separable representations.
• Empirical evaluations and integration with Vision Transformers highlight DDAEs’ adaptability, scalability, and performance improvements over supervised models.

An Overview of "Denoising Diffusion Autoencoders are Unified Self-supervised Learners"

The paper "Denoising Diffusion Autoencoders are Unified Self-supervised Learners" presents a comprehensive exploration into the intersection of generative and discriminative learning using diffusion models. This research investigates the capacity of denoising diffusion autoencoders (DDAE) to serve as both generative models and feature extractors for classification tasks, proposing a unified framework for self-supervised learning.

Diffusion Models and DDAEs

Diffusion models have emerged as state-of-the-art generative models, capable of producing high-fidelity images due to their multi-level denoising processes. In this framework, a series of Gaussian noise perturbations are applied to data inputs, which the model learns to invert effectively. This research extends the utility of these models to discriminative tasks by demonstrating that the intermediate layers of DDAEs can yield linear-separable features without additional encoders.

Evaluation of DDAEs

The paper presents empirical evaluations on benchmark datasets such as CIFAR-10 and Tiny-ImageNet. The authors conduct extensive experiments to determine optimal configurations for feature extraction, revealing that the combination of certain layers and noise levels significantly impacts the efficiency of linear probing.

Results:

• The DDAEs achieve 95.9% and 50.0% linear probe accuracies on CIFAR-10 and Tiny-ImageNet, respectively.
• Fine-tuning the truncated DDAE networks provides further performance improvements, surpassing supervised counterparts like WideResNet.
• The integration of DDAEs into Vision Transformers through transfer learning exemplifies their adaptability and scalability across architectures.

Analytical Insights

Key insights from the paper include the correlation between generative and discriminative capabilities. The authors observe that generative models with superior image quality tend to learn more effective representations, suggesting a deep interconnection between understanding and synthesis tasks. Ablations indicate that both noise level diversity and range contribute to improved feature learning.

Practical Implications

This research outlines several significant implications for future AI developments:

• Unified Learning Frameworks: DDAEs show potential as unified models that consolidate generative and discriminative tasks, simplifying architectures and reducing training complexity.
• Scalability with Transformers: The successful integration into Vision Transformers points toward scalable solutions applicable to large datasets.
• Efficiency and Transferability: The findings suggest pathways to leverage existing diffusion models in diverse applications beyond synthesis, such as recognition and classification.

Theoretical Speculations and Future Directions

The exploration into the potential unification of generative and discriminative learning paves the way for further research into optimizing architectures for dual-purpose tasks. The demonstrated efficacy of diffusion-based self-supervised learning could inspire the development of novel optimization strategies to enhance training efficiency, particularly within dense networks like Transformers.

Moreover, the potential alignment and uniformity in feature distributions may motivate new methodological approaches to improve feature extraction and model interpretability. Future investigations could focus on addressing current inefficiencies and further exploring pixel-space versus latent-space trade-offs in representation quality.

In conclusion, this paper highlights a promising direction in AI research, advancing the understanding of diffusion models as not only state-of-the-art generators but also as powerful discriminative learners. This dual capability could revolutionize the approach to model training, leading to more versatile, efficient, and scalable AI systems.