Inter-model Interpretability: Self-supervised Models as a Case Study. (arXiv:2207.11837v2 [cs.CV] UPDATED)

Since early machine learning models, metrics such as accuracy and precision
have been the de facto way to evaluate and compare trained models. However, a
single metric number doesn't fully capture the similarities and differences
between models, especially in the computer vision domain. A model with high
accuracy on a certain dataset might provide a lower accuracy on another
dataset, without any further insights. To address this problem we build on a
recent interpretability technique called Dissect to introduce
\textit{inter-model …

arxiv case case study cv interpretability model interpretability study