Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation
We introduce dataset interfaces, a scalable framework that synthesizes counterfactual examples under user-specified shiftsTailored Data Augmentation to Mitigate Model Failures
We demonstrate how we can use Stable Diffusion to target a model's failure modesModelDiff: A Framework for Comparing Learning Algorithms
We introduce a framework for comparing ML models trained with different learning algorithms.Raising the Cost of Malicious AI-Powered Image Editing
Inspired 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 Learning
We 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 Space
We demonstrate how to distill patterns of model errors as directions in a latent space.Uncovering Brittleness with Datamodels
In the second part of our datamodels series, we use datamodels to identify and study a new form of model brittleness.
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