DreamTeacher: Pretraining Image Backbones with Deep Generative Models (2307.07487v1)

Abstract: In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. We propose to distill knowledge from a trained generative model into standard image backbones that have been well engineered for specific perception tasks. We investigate two types of knowledge distillation: 1) distilling learned generative features onto target image backbones as an alternative to pretraining these backbones on large labeled datasets such as ImageNet, and 2) distilling labels obtained from generative networks with task heads onto logits of target backbones. We perform extensive analyses on multiple generative models, dense prediction benchmarks, and several pre-training regimes. We empirically find that our DreamTeacher significantly outperforms existing self-supervised representation learning approaches across the board. Unsupervised ImageNet pre-training with DreamTeacher leads to significant improvements over ImageNet classification pre-training on downstream datasets, showcasing generative models, and diffusion generative models specifically, as a promising approach to representation learning on large, diverse datasets without requiring manual annotation.

• The paper introduces diffusion-based feature distillation with Diffusion Transformers, achieving improved mIoU scores in segmentation tasks.
• It leverages robust backbones such as ConvNX-XL and Swin-L integrated with the ADM model to outperform traditional supervised methods on ADE20k and MS-COCO.
• The study highlights the method’s versatility and suggests future research in video understanding, 3D reconstruction, and autonomous driving applications.

Analyzing Advanced Techniques in Diffusion Model Feature Distillation for Visual Transfer Learning

Recent advancements in computer vision have underscored the importance of efficient feature distillation techniques, especially with the emergence of diffusion models. This paper presents a comprehensive evaluation of diffusion feature distillation, specifically in the context of visual transfer learning tasks, emphasizing the use of Diffusion Transformers (DT) combined with feature distillation architectures. The results documented in the paper provide a nuanced perspective on the potential of DT for a variety of complex vision tasks.

Overview of Diffusion Feature Distillation

Diffusion models have been receiving increased attention due to their robustness in image synthesis tasks. Specifically, the paper focuses on diffusion transformers with pretrained backbones like ConvNX-XL and Swin-L. The integration of ADM, a well-recognized diffusion model, has shown a successful transfer of learned features to downstream tasks. Unlike traditional GANs, diffusion models provide a probabilistic framework for image generation, offering a more nuanced feature representation that is beneficial for transfer learning.

Key Findings

The paper systematically presents transfer learning performance across various datasets, including ADE20k, MS-COCO, and BDD100K, using different modeling backbones and pretraining datasets. A notable observation is the superiority of DT combined with ADM in several benchmarks:

• Transfer Learning on MS-COCO and ADE20K: The experiments report compelling performance metrics with DT-enhanced pre-training. For instance, the mIoU scores for ADE20k using a ConvNX-XL backbone reach up to 69.3, surpassing conventional supervised learning baselines.
• Semantic and Instance Segmentation: Distillation with ADM significantly enhances mIoU scores. On BDD100K, the DT-feat.distil. approach with ConvNX-XL demonstrates an mIoU improvement to 69.3 from a baseline of 67.7 achieved by large-scale supervised networks.
• 3D Detection and Map Segmentation on nuScenes: The application of DT-feat.distil. sharply elevates performance metrics, notably improving MEAN IoU to 55.6 for the nuScenes dataset. These results manifest the advantages of diffusion model features in multi-modal fusion and joint learning strategies in autonomous driving scenarios.

Implications and Future Prospects

The findings from this paper suggest that diffusion models can effectively distill high-fidelity features capable of enhancing various computer vision tasks beyond typical generative use cases. The diffusion-based architectures clearly demonstrate superior generalization, especially where traditional training strategies falter.

This paper implies profound implications for the design of future visual models, advocating for feature distillation frameworks that exploit the stochastic nature of diffusion processes. It also lays the groundwork for extending diffusion techniques to further domains, such as video understanding and 3D reconstruction, by expanding the applications of DT-based frameworks.

Conclusion

The impactful augmentation of diffusion feature distillation techniques illustrated in this paper positions them as a viable option for advancing transfer learning in visual tasks. The demonstrated performance improvements across diverse benchmarks affirm the efficacy of diffusion models as feature learners and their promising applicability in domains requiring complex feature interactions. Future research prospects include refining these methodologies to optimize computational efficiency while maintaining or enhancing learning efficacy.