We lifted 6 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| valentyn1boreiko/svces_code | canonical | 4 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| Cifar100Wrapper | Ran | valentyn1boreiko/svces_code/utils/model_normalization.py code served (permissive licence) · get_code("af14c4446f7cffb4") |
| Cifar10Wrapper | Ran | valentyn1boreiko/svces_code/utils/model_normalization.py code served (permissive licence) · get_code("803b314109470237") |
| IdentityWrapper | Ran | valentyn1boreiko/svces_code/utils/model_normalization.py code served (permissive licence) · get_code("bbdb58215a0e1a06") |
| get_filename | Ran | valentyn1boreiko/svces_code/utils/load_trained_model.py code served (permissive licence) · get_code("7d476c2ca8c4113d") |
| build_model_big_transfer | Not yet run | valentyn1boreiko/svces_code/utils/models/big_transfer_factory.py code served (permissive licence) · get_code("c0da9b2317282066") |
| get_TinyImageNetClassNames | Not yet run | valentyn1boreiko/svces_code/utils/datasets/tiny_image_net.py code served (permissive licence) · get_code("16ac425097ec58cd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Visual counterfactual explanations (VCEs) in image space are an important tool to understand decisions of image classifiers as they show under which changes of the image the decision of the classifier would change. Their generation in image space is challenging and requires robust models due to the problem of adversarial examples. Existing techniques to generate VCEs in image space suffer from spurious changes in the background. Our novel perturbation model for VCEs together with its efficient optimization via our novel Auto-Frank-Wolfe scheme yields sparse VCEs which lead to subtle changes specific for the target class. Moreover, we show that VCEs can be used to detect undesired behavior of ImageNet classifiers due to spurious features in the ImageNet dataset.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2205.07972")
get_code_for_paper("2205.07972")
have("2205.07972")
Connect an agent — have() is free.