Jun Zhu, Chongxuan Li, Fan Bao, Jiacheng Sun, Zebin You, Yong Zhong
We lifted 22 functions out of this paper's own repositories and ran 17 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 |
|---|---|---|
| ML-GSAI/DPT | canonical | 11 of 16 |
| copy not recorded | — | 3 of 3 |
| baofff/u-vit | extension | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| center_crop | Ran | ML-GSAI/DPT/datasets.py code served (permissive licence) · get_code("f057bf3471d77c43") |
| distributed_sinkhorn | Ran | ML-GSAI/DPT/src/losses.py code served (permissive licence) · get_code("7478f8e64cee8964") |
| drop_path | Ran | ML-GSAI/DPT/src/deit.py code served (permissive licence) · get_code("55120f2026b56aa2") |
| get_sde | Ran | ML-GSAI/DPT/sde.py code served (permissive licence) · get_code("b723a1cb4778012f") |
| interpolate_fn | Ran | ML-GSAI/DPT/dpm_solver_pp.py code served (permissive licence) · get_code("86c7877e164664bc") |
| make_transforms | Ran | ML-GSAI/DPT/src/data_manager.py code served (permissive licence) · get_code("cec1c5fdf881ddf4") |
| model_wrapper | Ran | ML-GSAI/DPT/dpm_solver_pp.py code served (permissive licence) · get_code("ace7da590f4070e0") |
| model_wrapper | Ran | ML-GSAI/DPT/dpm_solver_pytorch.py code served (permissive licence) · get_code("b102ed94bbf0cfb9") |
| mos | Ran | ML-GSAI/DPT/sde.py code served (permissive licence) · get_code("02c82bdf3494ea71") |
| patchify | Ran | this paper's copy was not recorded; identical code first harvested from baofff/U-ViT pointer only · get_code("7a7de2a8e080f047") |
| patchify | Ran | baofff/u-vit/libs/uvit.py code served (permissive licence) · get_code("9cf076a2ae913f94") |
| random_crop_arr | Ran | ML-GSAI/DPT/datasets.py code served (permissive licence) · get_code("05d1f95a391c0ec3") |
| stable_diffusion_beta_schedule | Ran | ML-GSAI/DPT/sample_ldm_discrete_all.py code served (permissive licence) · get_code("2ee6e353f1dd4cf3") |
| timestep_embedding | Ran | this paper's copy was not recorded; identical code first harvested from baofff/U-ViT pointer only · get_code("262b18d8278dcf70") |
| timestep_embedding | Ran | baofff/u-vit/libs/uvit.py code served (permissive licence) · get_code("b494dd297ded5960") |
| unpatchify | Ran | this paper's copy was not recorded; identical code first harvested from baofff/U-ViT pointer only · get_code("82e1ad8a9b00554e") |
| unpatchify | Ran | baofff/u-vit/libs/uvit.py code served (permissive licence) · get_code("f9244c08c1011341") |
| center_crop_arr | Not yet run | ML-GSAI/DPT/datasets.py code served (permissive licence) · get_code("f8b4a29a52612a41") |
| copy_imgnt_locally | Not yet run | ML-GSAI/DPT/src/data_manager.py code served (permissive licence) · get_code("b2c367ab087ec039") |
| init_data | Not yet run | ML-GSAI/DPT/src/data_manager.py code served (permissive licence) · get_code("ec08cc8c5589ddfe") |
| load_checkpoint | Not yet run | ML-GSAI/DPT/src/msn_train.py code served (permissive licence) · get_code("e22a49db490231b6") |
| stp | Not yet run | ML-GSAI/DPT/sde.py code served (permissive licence) · get_code("4d33c2f525f1b1c0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models. DPT operates in three stages: training a classifier on partially labeled data to predict pseudo-labels; training a conditional generative model using these pseudo-labels to generate pseudo images; and retraining the classifier with a mix of real and pseudo images. Empirically, DPT consistently achieves SOTA performance of semi-supervised generation and classification across various settings. In particular, with one or two labels per class, DPT achieves a Fréchet Inception Distance (FID) score of 3.08 or 2.52 on ImageNet 256 × 256. Besides, DPT outperforms competitive semi-supervised baselines substantially on ImageNet classification tasks, achieving top-1 accuracies of 59.0 (+2.8), 69.5 (+3.0), and 74.4 (+2.0) with one, two, or five labels per class, respectively. Notably, our results demonstrate that diffusion can generate realistic images with only a few labels (e.g., < 0.1%) and generative augmentation remains viable for semi-supervised classification. Our code is available at https://github.com/ML-GSAI/DPT.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.10586")
get_code_for_paper("2302.10586")
have("2302.10586")
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