Tailored Data Augmentation to Mitigate Model FailuresWe demonstrate how we can use Stable Diffusion to target a model's failure modes
ModelDiff: A Framework for Comparing Learning AlgorithmsWe introduce a framework for comparing ML models trained with different learning algorithms.
PhotoGuard: Defending Against Diffusion-based Image ManipulationInspired by an episode of the Daily Show, we hacked together a technique for "immunizing" images against being edited by diffusion models.
A Data-Based Perspective on Transfer LearningWe present a framework for pinpointing the impact of the source datasets in transfer learning.
When does Bias Transfer in Transfer Learning?We demonstrate that biases from pre-trained models can persist even after fine-tuning.
Distilling Model Failures as Directions in Latent SpaceWe demonstrate how to distill patterns of model errors as directions in a latent space.
Uncovering Brittleness with DatamodelsIn the second part of our datamodels series, we use datamodels to identify and study a new form of model brittleness.
Missingness Bias in Model DebuggingWe demonstrate how current missingness approximations introduce biases into model debugging.