Abstract: Recently masked autoencoder ... that the model can learn robust saliency prior knowledge from the reconstructed images. Besides, we propose a simple and novel network EANet driven by DHMMAE ...

but the sequential prediction process is much faster than diffusion. These models use representations known as tokens to make predictions. An autoregressive model utilizes an autoencoder to ...

Inception, a new Palo Alto-based company started by Stanford computer science professor Stefano Ermon, claims to have developed a novel AI model based on “diffusion” technology. Inception ...

By parameterizing the denoising autoencoder as a low-rank model, it is shown that optimizing the training loss of diffusion models is equivalent to solving a subspace clustering problem. This ...

2412.17302v1 link 2024-12-23 STeInFormer: Spatial-Temporal Interaction Transformer Architecture for Remote Sensing Change Detection Xiaowen ... 2410.18580v1 null 2024-10-14 Graph Masked Autoencoder ...

Unlike prior autoencoder-based diffusion models, Stable Diffusion incorporates a U-Net backbone with cross-attention layers to reduce noise while learning the latent representation. This enables the ...

[PDF][Code] Mask-guided Spectral-wise Transformer for Efficient ... [PDF] [Code] Spectral-Cascaded Diffusion Model for Remote Sensing Image Spectral Super-Resolution，TGRS 2024, Bowen Chen, et al. [PDF ...