We lifted 12 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| pankajmishra000/VT-ADL | canonical | 9 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| Filter | Ran | pankajmishra000/VT-ADL/utility_fun.py code served (permissive licence) · get_code("bd26390e250ca920") |
| Mean_var | Ran | pankajmishra000/VT-ADL/utility_fun.py code served (permissive licence) · get_code("3467f0a16adaaa21") |
| Normalise | Ran | pankajmishra000/VT-ADL/utility_fun.py code served (permissive licence) · get_code("86593bc2833571ad") |
| Test_anom_data | Ran | pankajmishra000/VT-ADL/mvtech.py code served (permissive licence) · get_code("87b687480ed0426e") |
| add_noise | Ran | pankajmishra000/VT-ADL/VT_AE.py code served (permissive licence) · get_code("7dd161c6f0758351") |
| load_images | Ran | pankajmishra000/VT-ADL/BT_dataset.py code served (permissive licence) · get_code("53d3badfd3821380") |
| load_images | Ran | pankajmishra000/VT-ADL/mvtech.py code served (permissive licence) · get_code("b31bd461bb9d9b0c") |
| load_masks | Ran | pankajmishra000/VT-ADL/BT_dataset.py code served (permissive licence) · get_code("a48b0717b38868b6") |
| read_files | Ran | pankajmishra000/VT-ADL/mvtech.py code served (permissive licence) · get_code("501c9b90f86e3173") |
| log_gaussian | Not yet run | pankajmishra000/VT-ADL/mdn1.py code served (permissive licence) · get_code("fbaeafa76f30d8d1") |
| log_gmm | Not yet run | pankajmishra000/VT-ADL/mdn1.py code served (permissive licence) · get_code("65bded0c0bcd4ebb") |
| mdn_loss_function | Not yet run | pankajmishra000/VT-ADL/mdn1.py code served (permissive licence) · get_code("961240dd7abf50e6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps to preserve the spatial information of the embedded patches, which are later processed by a Gaussian mixture density network to localize the anomalous areas. In addition, we also publish BTAD, a real-world industrial anomaly dataset. Our results are compared with other state-of-the-art algorithms using publicly available datasets like MNIST and MVTec.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.10036")
get_code_for_paper("2104.10036")
have("2104.10036")
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