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Found 7 papers, 3 papers with code
A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models
The rise of foundation models has transformed machine learning research, prompting efforts to uncover their inner workings and develop more efficient and reliable applications for better control.
Understanding and Mitigating Compositional Issues in Text-to-Image Generative Models
We also show that re-weighting the erroneous attention contributions in CLIP can also lead to improved compositional performances, however these improvements are often less significant than those achieved by solely learning a linear projection head, highlighting erroneous attentions to be only a minor error source.
DREW : Towards Robust Data Provenance by Leveraging Error-Controlled Watermarking
Identifying the origin of data is crucial for data provenance, with applications including data ownership protection, media forensics, and detecting AI-generated content.
Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning
Vision transformers have established a precedent of patchifying images into uniformly-sized chunks before processing.
Enhancing Epileptic Seizure Detection with EEG Feature Embeddings
Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal.
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
For this purpose, we first demonstrate that the existing model-based methods can be equivalent to applying smaller perturbation or optimization weights to the hard training examples.
Your Out-of-Distribution Detection Method is Not Robust!
Therefore, unlike OOD detection in the standard setting, access to OOD, as well as in-distribution, samples sounds necessary in the adversarial training setup.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
