MaCO, ForGRAD and a Torch Wrapper
Release note v1.1.0
New Features
MaCO
Introduction of a recent method for scaling up feature visualization on state-of-the-art deep models: MaCo. This method is described in the following arXiv paper: https://arxiv.org/pdf/2306.06805.pdf. It involves fixing the amplitude in the Fourier spectrum and only optimizing the phase during the optimization of a neuron/channel/layer.
It comes with the associated documentation, tests and notebook
FORGrad
Introduction of FORGrad (paper here: https://arxiv.org/pdf/2307.09591.pdf). All in all, this method consists in filtering the noise in the explanations to make them more interpretable.
It comes with the associated documentation, tests and notebook
PyTorch Wrapper
Provide within Xplique a convenient wrapper for Pytorch's model that works for most attribution methods and is compatible with metrics.
It comes with the associated documentation, tests and notebook. It also introduce its own pipeline for CI challenging the cross-version between TF and PyTorch.
Introduce a Tasks Enum
Add the Tasks enum which includes the operators for classification and regression tasks. The possibility to choose from the existing operator by their name was added.
Add an activation parameter for metrics
While we recommend using the logits to generate explanations, it might be more relevant to look at the probability (after a softmax or sigmoid layer) of a prediction when computing metrics for instance if it measures 'a drop in probability for the classification of an input occluded in the more relevant part'. Thus, we introduce this option when you build a metric. activation can be either None, 'softmax' or 'sigmoid'.
Bug fixes
regression_operator
The operator was a sum instead of a mean (for MAE). It has been fixed.
HSIC attribution
- doc of call of HSICEstimator
- Add @tf.function
Documentation
- Enhance overall the documentation
- Add documetation for the
operator - Add explanation concerning the model, the API
- Add a pipeline for CI of the documentation