Search Results for author:
Found 2 papers, 2 papers with code
Model Reprogramming Outperforms Fine-tuning on Out-of-distribution Data in Text-Image Encoders
When evaluating the performance of a pre-trained model transferred to a downstream task, it is imperative to assess not only the in-distribution (ID) accuracy of the downstream model but also its capacity to generalize and identify out-of-distribution (OOD) samples.
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Detecting out-of-distribution (OOD) data has become a critical component in ensuring the safe deployment of machine learning models in the real world.
Ranked #12 on
Out-of-Distribution Detection
on ImageNet-1k vs SUN
