Search Results for author:
Found 7 papers, 3 papers with code
Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP
Recent works have explored how individual components of the CLIP-ViT model contribute to the final representation by leveraging the shared image-text representation space of CLIP.
Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models
Recent text-to-image generative models such as Stable Diffusion are extremely adept at mimicking and generating copyrighted content, raising concerns amongst artists that their unique styles may be improperly copied.
Exploring Geometry of Blind Spots in Vision Models
We further observe that the source image is linearly connected by a high-confidence path to these inputs, uncovering a star-like structure for level sets of deep networks.
Can AI-Generated Text be Reliably Detected?
In particular, we develop a recursive paraphrasing attack to apply on AI text, which can break a whole range of detectors, including the ones using the watermarking schemes as well as neural network-based detectors, zero-shot classifiers, and retrieval-based detectors.
Towards Improved Input Masking for Convolutional Neural Networks
In this work, we propose a new masking method for CNNs we call layer masking in which the missingness bias caused by masking is reduced to a large extent.
Simulating Network Paths with Recurrent Buffering Units
Simulating physical network paths (e. g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking.
What's in a Name? Are BERT Named Entity Representations just as Good for any other Name?
We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input.
