# Natural Language Descriptions of Deep Visual Features (@ ICLR 2022)

### Still anonymous

The results look very promising: they use it to analyze important features in a few different architectures (and show that results generalize well to architectures held-out during training), to audit models that are supposed to not care about certain protected features, and finally to analyze spurious correlations learned in adversarial dataset (also improve the model zero-shot'' by just removing the neurons corresponding to spurious features). This should enable a range of new applications, and scaling this up (which I think the likes of Google & FAIR/MAIR might do) can likely step up the current state of interpretability quite significantly.