21 citations · 54 across the 9 of their papers we have counts for
4 papers · 1 filter
Unlearning That Lasts: Utility-Preserving, Robust, and Almost Irreversible Forgetting in LLMs
Naman Deep Singh, Maximilian Müller, Francesco Croce +1
Unlearning in large language models (LLMs) involves precisely removing specific information from a pre-trained model. This is crucial to ensure safety of LLMs by deleting private d…
Robustness in Both Domains: CLIP Needs a Robust Text Encoder
Elias Abad Rocamora, Christian Schlarmann, Naman Deep Singh +3
Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-…
Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
Amit Peleg, Naman Deep Singh, Matthias Hein
Vision-language models like CLIP have demonstrated remarkable zero-shot capabilities in classification and retrieval. However, these models often struggle with compositional reason…
Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics
Francesco Croce, Christian Schlarmann, Naman Deep Singh +1
Measuring perceptual similarity is a key tool in computer vision. In recent years perceptual metrics based on features extracted from neural networks with large and diverse trainin…