Neural Networks and Spurious Correlations
Explore neural networks and their susceptibility to spurious correlations in this 40-minute lecture by Aditi Raghunathan from UC Berkeley. Delve into the challenges of distribution shifts and their impact on model performance. Learn about techniques to train neural networks that are robust to known spurious correlations, including counter-intuitive observations on model size and training data effects. Examine approaches to mitigate spurious correlations when complete information is unavailable. Investigate the empirical performance of reweighting and subsampling methods, focusing on tasks like bird type classification with spurious backgrounds. Gain insights into why neural networks pick up on spurious correlations, understand the simplicity bias, and discover strategies to overcome it. Conclude with valuable parting thoughts on improving neural network robustness in the presence of spurious correlations.