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cs.LG2024

Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models

Regev Cohen, Idan Kligvasser, Ehud Rivlin +1

The pursuit of high perceptual quality in image restoration has driven the development of revolutionary generative models, capable of producing results often visually indistinguish…

cs.CV2024

Anchored Diffusion for Video Face Reenactment

Idan Kligvasser, Regev Cohen, George Leifman +2

Video generation has drawn significant interest recently, pushing the development of large-scale models capable of producing realistic videos with coherent motion. Due to memory co…

cs.SD2024

On the Semantic Latent Space of Diffusion-Based Text-to-Speech Models

Miri Varshavsky-Hassid, Roy Hirsch, Regev Cohen +3

The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech (TTS) domain is rising, providing great value in synthesizing high quality speech. Although they exhibi…

cs.AI2024

Capabilities of Gemini Models in Medicine

Khaled Saab, Tao Tu, Wei-Hung Weng +64

Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of…

eess.IV2024

Predicting Generalization of AI Colonoscopy Models to Unseen Data

Joel Shor, Carson McNeil, Yotam Intrator +15

: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on…

cs.CL2024

Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?

Yotam Intrator, Matan Halfon, Roman Goldenberg +5

Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the wides…