Removing Spurious Concepts from Neural Network Representations via Joint
  Subspace Estimation
Floris Holstege Bram Wouters Noud van Giersbergen Cees Diks
Out-of-distribution generalization in neural networks is often hampered by
spurious correlations. A common strategy is to mitigate this by removing
spurious concepts from the neural network representation of the data. Existing
concept-removal methods tend to be overzealous by inadvertently eliminating
features associated with the main task of the model, thereby harming model
performance. We propose an iterative algorithm that separates spurious from
main-task concepts by jointly identifying two low-dimensional orthogonal
subspaces in the neural network representation. We evaluate the algorithm on
benchmark datasets for computer vision (Waterbirds, CelebA) and natural
language processing (MultiNLI), and show that it outperforms existing concept
removal methods