We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sndnyang/Diffusion_ViT | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| run_bpd_on_dataset | Not yet run | sndnyang/Diffusion_ViT/eval_model.py code served (permissive licence) · get_code("01b15fb2a5c20832") |
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Diffusion Denoising Probability Models (DDPM) and Vision Transformer (ViT) have demonstrated significant progress in generative tasks and discriminative tasks, respectively, and thus far these models have largely been developed in their own domains. In this paper, we establish a direct connection between DDPM and ViT by integrating the ViT architecture into DDPM, and introduce a new generative model called Generative ViT (GenViT). The modeling flexibility of ViT enables us to further extend GenViT to hybrid discriminative-generative modeling, and introduce a Hybrid ViT (HybViT). Our work is among the first to explore a single ViT for image generation and classification jointly. We conduct a series of experiments to analyze the performance of proposed models and demonstrate their superiority over prior state-of-the-arts in both generative and discriminative tasks. Our code and pre-trained models can be found in https://github.com/sndnyang/Diffusion_ViT .
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2208.07791")
get_code_for_paper("2208.07791")
have("2208.07791")
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