93 citations · 446 across the 32 of their papers we have counts for
4 papers · 1 filter
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
Damien Teney, Kushal Kafle, Robik Shrestha +3
Out-of-distribution (OOD) testing is increasingly popular for evaluating a machine learning system's ability to generalize beyond the biases of a training set. OOD benchmarks are d…
Visual Question Answering with Prior Class Semantics
Violetta Shevchenko, Damien Teney, Anthony Dick +1
We present a novel mechanism to embed prior knowledge in a model for visual question answering. The open-set nature of the task is at odds with the ubiquitous approach of training…
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision
Damien Teney, Ehsan Abbasnedjad, Anton van den Hengel
One of the primary challenges limiting the applicability of deep learning is its susceptibility to learning spurious correlations rather than the underlying mechanisms of the task…
Unshuffling Data for Improved Generalization
Damien Teney, Ehsan Abbasnejad, Anton van den Hengel
Generalization beyond the training distribution is a core challenge in machine learning. The common practice of mixing and shuffling examples when training neural networks may not…