Tag
PnP-CoSMo is a plug-and-play framework for multi-contrast MRI reconstruction that learns content/style models from image data, enabling reconstruction without raw k-space training data. It is generalizable across contrasts and forward operators.
This paper challenges the prevailing view that rote memorization causes training data exposure to reconstruction attacks, showing instead that adversarial non-robust features are the true cause. The authors introduce AntiAdversarial Training (AT-AT) that intentionally learns non-robust features to achieve superior reconstruction defense and higher accuracy.
IDEAL proposes an in-depth alignment framework for discrete representation autoencoding, jointly aligning quantized tokens with shallow and deep VFM features to achieve superior reconstruction and generation performance.
DecQ introduces lightweight detail-condensing queries to improve reconstruction and generation in representation autoencoders without disrupting pretrained semantic spaces.
This paper introduces a new energy-based model for linear inverse problems that learns normalized posterior densities, overcoming limitations of diffusion models. It enables unbiased sampling, adaptive sampling, and blind degradation estimation, with competitive performance on ImageNet, CelebA, and AFHQ.
Qwen-Image-VAE-2.0 is a high-compression Variational Autoencoder suite that improves reconstruction fidelity and diffusability through enhanced architecture, large-scale training, and semantic alignment strategies.
Elon Musk explains that Tesla FSD utilizes AI photon count reconstruction rather than standard RGB, enabling superior performance in low-light and high-glare conditions.