Implementation of Gradient Agreement Filtering, from Chaubard et al. of Stanford, but for single machine microbatches, in Pytorch

The proposed clustering-based interpretability method enhances the robustness of neural network explanations by grouping similar feature maps and weighting them according to their gradients, thereby reducing noise and capturing more comprehensive activation patterns.