6 papers
Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem
Amit Peleg, Naman Deep Singh, Naama Pearl +2
Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety. Yet existing benchmarks measure i…
Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP
Naman Deep Singh, Francesco Croce, Matthias Hein
Vision-Language models like CLIP have been shown to be highly effective at linking visual perception and natural language understanding, enabling sophisticated image-text capabilit…
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…
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-…
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…
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…