Pietro Liò, Dmitry Kazhdan, Adrian Weller, Mateja Jamnik, Botty Dimanov, Helena Terre
We lifted 19 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| dmitrykazhdan/concept-based-xai | canonical | 10 of 19 |
| Function | Status | Where it lives |
|---|---|---|
| built_task_fn | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/latentFactorData.py code served (permissive licence) · get_code("f859bf3dd114e7d7") |
| cardinality_encoding | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/dSprites.py code served (permissive licence) · get_code("b19d09ea8a8e0374") |
| compute_activation_per_layer | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/methods/CME/ItCModel.py code served (permissive licence) · get_code("b3d3a0d10f9d6c5f") |
| flatten_activations | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/methods/CME/ItCModel.py code served (permissive licence) · get_code("d9c290aa08862671") |
| get_latent_bases | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/dataset_utils.py code served (permissive licence) · get_code("0b50f14e6d4b1ad7") |
| get_task_data | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/latentFactorData.py code served (permissive licence) · get_code("1fa117d288391f24") |
| latent_to_index | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/dataset_utils.py code served (permissive licence) · get_code("681a89569c3f3cb2") |
| load_dataset_paths | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/load_paths.py code served (permissive licence) · get_code("ed967be24bf64400") |
| small_skip_ranges_filter_fn | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/dSprites.py code served (permissive licence) · get_code("cf5d142e3d671d42") |
| small_skip_ranges_filter_fn | Ran | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/shapes3D.py code served (permissive licence) · get_code("72718db40bbd6986") |
| get_all_concepts | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/cars3D.py code served (permissive licence) · get_code("794a85c6f0e9d261") |
| get_category_full | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/smallNorb.py code served (permissive licence) · get_code("baf0160539f306cc") |
| get_elevation_full | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/cars3D.py code served (permissive licence) · get_code("db1bb50b81c139cb") |
| get_model | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/utils/model_loader.py code served (permissive licence) · get_code("58f491becc62bed0") |
| get_shape_full | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/dSprites.py code served (permissive licence) · get_code("17598bb41881ef43") |
| get_shape_full | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/shapes3D.py code served (permissive licence) · get_code("54b1d202a35ed398") |
| get_shape_small_skip | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/shapes3D.py code served (permissive licence) · get_code("1e0aa241637e9968") |
| load_mesh | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/datasets/cars3D.py code served (permissive licence) · get_code("bca977322866f5f2") |
| produce_bottleneck | Not yet run | dmitrykazhdan/concept-based-xai/concepts_xai/methods/CBM/CBModel.py code served (permissive licence) · get_code("7bde78aa9a7f31ce") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Concept-based explanations have emerged as a popular way of extracting humaninterpretable representations from deep discriminative models. At the same time, the disentanglement learning literature has focused on extracting similar representations in an unsupervised or weakly-supervised way, using deep generative models. Despite the overlapping goals and potential synergies, to our knowledge, there has not yet been a systematic comparison of the limitations and trade-offs between concept-based explanations and disentanglement approaches. In this paper, we give an overview of these fields, comparing and contrasting their properties and behaviours on a diverse set of tasks, and highlighting their potential strengths and limitations. In particular, we demonstrate that state-of-the-art approaches from both classes can be data inefficient, sensitive to the specific nature of the classification/regression task, or sensitive to the employed concept representation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.06917")
get_code_for_paper("2104.06917")
have("2104.06917")
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