captum VS interpret

Most likely, NNs in general love shortcut learning (see Geirhos et al. 2020). In general, local explanations such as grad-cam are quite noisy, and sometimes even inconsistent (see Seo et al. 2018 ). Now, in my experience, I've seen that integrated gradients (see Sundararajan et al 2017) does a better job compared to Grad-CAM (also, add a noise tunnel), but this is only based on my limited experience. I would totally recommend using the implementations from the Captum library for loca explanations.

Take a look at captum

Sometimes. If explainable predictions are part of your business requirements, it's probably better not to rely entirely on black box models and instead design a system that gives you the information you need as part of it. If you end up using black box models, there are still methods that attempt to help attribute explanations to your prediction. Here's an example of a toolkit for attributing explanations post-hoc to black box model predictions: https://github.com/pytorch/captum

The straightforward way to interpret RL agent's decision is to use captum library.

My use-case is research-oriented. I work on Explainable AI. Generally, the best package I've come across to compute attributions in Pytorch Captum. If your object detector is in PyTorch, you can perhaps build it in.

Do any JAX experts know if there is an equivalent to https://captum.ai/ - a model interpretability library for pytorch?

In particular i want to be able to measure feature importance on both inputs and internal layers on a sample by sample basis. This is the only thing currently holding me back from using JAX right now.

Alternatively a simle to read/understand/port implementation of DeepLIFT would work too.

thanks

I'm interested to use JAX but am having a hard time finding anything similar to captum for the pytorch world.

Do you actually want to implement something? There are decent explainability libraries now, e.g., [AIX360](https://aix360.mybluemix.net/), [InterpretML](https://interpret.ml/), or [captum](https://captum.ai/). Pytorch + maybe pytorch lightning + captum might be the quickest way to actually implement something like an explainable neural net yourself. Do the standard tutorials for each of them and watch a few YT videos (or follow a coursera course or something like that) about how these things work in theory and practice, and you'll get up to speed relatively quickly. You will not be able to do useful work in ML without actually learning the ropes.

Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.

You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret

They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)

Disclosure: I helped build this open source package

Microsoft Explainable Boosting Machine (which is a Gaussian Additive Model and not a Gradient Boosted Trees 🙄 model) is a step in that direction https://github.com/interpretml/interpret

Also I'd recommend everyone who works with xgboost to give EBM's a try! They perform comparably (except in the case of extreme interactions) but are actually interpretable! https://github.com/interpretml/interpret/ Beside that they since on runtime they're practically a lookup table they're very quick (at the cost of longer training time).

Open source code by Microsoft: https://github.com/interpretml/interpret (called EBM in this implementation).

If it's not an image, have a go at Microsoft's Explainable Boosting Maching) https://github.com/interpretml/interpret which is not a GBM but a GAM (Gradient Boosting Machine vs Gradient Additive Model). This will also give you explanation via SHAP or LIME values.