Zobrazeno 1 - 10
of 6 371
pro vyhledávání: '"A Michaeli"'
Photo-realistic image restoration algorithms are typically evaluated by distortion measures (e.g., PSNR, SSIM) and by perceptual quality measures (e.g., FID, NIQE), where the desire is to attain the lowest possible distortion without compromising on
Externí odkaz:
http://arxiv.org/abs/2410.00418
Diffractive neural networks hold great promise for applications requiring intensive computational processing. Considerable attention has focused on diffractive networks for either spatially coherent or spatially incoherent illumination. Here we illus
Externí odkaz:
http://arxiv.org/abs/2408.06681
Foundations of formal languages, as subfield of theoretical computer science, are part of typical upper secondary education curricula. There is very little research on the potential difficulties that students at this level have with this subject. In
Externí odkaz:
http://arxiv.org/abs/2409.15043
Diffusion models dominate the field of image generation, however they have yet to make major breakthroughs in the field of image compression. Indeed, while pre-trained diffusion models have been successfully adapted to a wide variety of downstream ta
Externí odkaz:
http://arxiv.org/abs/2407.09896
Autor:
Chefer, Hila, Zada, Shiran, Paiss, Roni, Ephrat, Ariel, Tov, Omer, Rubinstein, Michael, Wolf, Lior, Dekel, Tali, Michaeli, Tomer, Mosseri, Inbar
Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primar
Externí odkaz:
http://arxiv.org/abs/2407.08674
Compressed Sensing (CS) facilitates rapid image acquisition by selecting a small subset of measurements sufficient for high-fidelity reconstruction. Adaptive CS seeks to further enhance this process by dynamically choosing future measurements based o
Externí odkaz:
http://arxiv.org/abs/2407.08256
Autor:
Michaeli, Eyal, Fried, Ohad
Fine-grained visual classification (FGVC) involves classifying closely related sub-classes. This task is difficult due to the subtle differences between classes and the high intra-class variance. Moreover, FGVC datasets are typically small and challe
Externí odkaz:
http://arxiv.org/abs/2406.14551
Training deep neural networks requires significant computational resources and large datasets that are often confidential or expensive to collect. As a result, owners tend to protect their models by allowing access only via an API. Many works demonst
Externí odkaz:
http://arxiv.org/abs/2406.00828
When solving ill-posed inverse problems, one often desires to explore the space of potential solutions rather than be presented with a single plausible reconstruction. Valuable insights into these feasible solutions and their associated probabilities
Externí odkaz:
http://arxiv.org/abs/2405.15719
Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive. They consider a reconstruction to be a correct outcome for a group (
Externí odkaz:
http://arxiv.org/abs/2405.13805