The goal of data attribution is to trace model predictions back to training data. Despite a long
line of work towards this goal, existing approaches to data attribution tend to force users to …

How do two sets of images differ? Discerning set-level differences is crucial for
understanding model behaviors and analyzing datasets yet manually sifting through …

Computer vision datasets frequently contain spurious correlations between task-relevant
labels and (easy to learn) latent task-irrelevant attributes (eg context). Models trained on …

Measuring similarity of neural networks to understand and improve their behavior has
become an issue of great importance and research interest. In this survey, we provide a …

Data augmentation (DA) encodes invariance and provides implicit regularization critical to a
model's performance in image classification tasks. However, while DA improves average …

Biases in models pose a critical issue when deploying machine learning systems, but
diagnosing them in an explainable manner can be challenging. To address this, we …

While deep learning has led to huge progress in complex image classification tasks like
ImageNet unexpected failure modes eg via spurious features call into question how reliably …

How does the internal computation of a machine learning model transform inputs into
predictions? In this paper, we introduce a task called component modeling that aims to …

Diffusion models trained on large datasets can synthesize photo-realistic images of
remarkable quality and diversity. However, attributing these images back to the training data …

Machine learning models can fail on subgroups that are underrepresented during training.
While techniques such as dataset balancing can improve performance on underperforming …