Where is the Truth? The Risk of Getting Confounded in a Continual World
Florian Peter Busch Roshni Kamath Rupert Mitchell Wolfgang Stammer Kristian Kersting Martin Mundt
A dataset is confounded if it is most easily solved via a spurious
correlation which fails to generalize to new data. We will show that, in a
continual learning setting where confounders may vary in time across tasks, the
resulting challenge far exceeds the standard forgetting problem normally
considered. In particular, we derive mathematically the effect of such
confounders on the space of valid joint solutions to sets of confounded tasks.
Interestingly, our theory predicts that for many such continual datasets,
spurious correlations are easily ignored when the tasks are trained on jointly,
but it is far harder to avoid confounding when they are considered
sequentially. We construct such a dataset and demonstrate empirically that
standard continual learning methods fail to ignore confounders, while training
jointly on all tasks is successful. Our continually confounded dataset, ConCon,
is based on CLEVR images and demonstrates the need for continual learning
methods with more robust behavior with respect to confounding.