Comments (2)
I think that we should settle for a model(s?) and a dataset before thinking about XAI.
You can use Captum on multiple type of models - if we want to have some XAI demo we could include:
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BioBERT 1 for some NLP task; Captum with BERT on SQuAD 2 - word importance, layer interpretability, attention masks).
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Some kind of segmentation CNN; ResNet segmentation ablation 3 - this could be cool if we find a dataset in which we can segment more than one class; perturbation, feature ablation 4
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Image classification model; ResNet interpretability 5 - different gradient-based attributions
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VQA would be awesome, I think that this would be a killer demo for ML on medical data + XAI 6, but I don't know if there's any medical dataset made specifically for VQA, maybe look into it when solving #33 ?
Every method model requirements can be found on 7. A lot of them just require the functions to be differentiable, so we'll probably be able to do some XAI on vision models.
The web interface is nice, it can also be run in jupyter notebooks, I think we could also hack it and add it into a web app.
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I'm gonna look into this one
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Related Issues (20)
- Should we merge it in tensorboard/pytorch?
- XAI nn.Module
- Translate Issues
- Federated learning using Syft + Grid
- Geo-Location and possibly more metadata
- Inference on a remote model
- Medical MVP: Choose a dataset for the demo HOT 2
- Medical MVP: Train a MONAI architecture over a dataset and export it to TorchScript HOT 1
- Medical MVP: Expose the model in the cloud HOT 2
- Medical MVP: Allocate a domain, a TLS certificate and a public IP for our medical API HOT 1
- Medical MVP: Simple app that uses our API
- Medical MVP: App enhancements
- Medical MVP: MONAI research
- Medical MVP: Adding federated learning stub
- Medical MVP: Private inference
- Medical MVP: Captum XAI demos
- Medical MVP: VQA viability HOT 2
- Medical MVP: VQA+Captum demo HOT 3
- Medical MVP: Model training infrastructure HOT 3
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