Excitation Pullbacks: Making AI Transparent
Our method shows what the model really looks at when making a prediction.
It amplifies the most important features for the chosen label,
and these features turn out to align surprisingly well with human perception.
This makes AI decisions easier to understand and more transparent.
Future work will enable neuron-specific adjustment of the "temp" hyperparameter, which is expected to significantly enhance explanation quality.
For details, check out our paper and its corresponding code repository.