“Photonic unsupervised learning variational autoencoder for high-throughput and low- latency image transmission.” Sci. Adv. 9, eadf8437437 (2023). Google Scholar Li, J. et al. “Spectrally encoded single-pixel machine vision using diffractive networks.” Sci. Adv. 7, eabd7690 (2021). ...
由于扩散模型(Diffusion Models, DM)在训练过程中计算资源需求很高,研究人员选择在强大的预训练自动编码器(autoencoders)的潜在空间(latent space)中应用这些模型,以在保留模型质量和灵活性的同时,减少计算资源的需求。 复杂度与细节保留的优化: 与之前的工作相比,将扩散模型应用于潜在空间,首次达到了复杂度降低与细节...
First, an autoencoder (AE) compresses the data into a lower dimensional representation. Second, the multivariate distribution of the encoded data is estimated with vine copulas. Third, a generative model is obtained by combining the estimated distribution with the decoder part of the AE. As such...
this requires a lot of disk space and time. If you already have ImageNet on your disk, you can speed things up by putting the data into${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/(which defaults to~/.cache/autoencoders/data/ILSVRC2012_{split}/data/), where{split}is one...
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows to apply them to image modification tasks such as inpainting directly ...
On the structure data, distilling the XRD data into the latent space using autoencoders revealed that the low-dimensional components z2 and z4 spanned the largest range (0 to 6), z1 and z5 spanned a smaller range (0 to 3), and z3 was nearly zero for all compositions. The near-...
highly interacting genes may serve as breast cancer indicators that merit further investigation. After training the SDAE, they chose a layer with a low dimension and validation error compared to other encoder stacks. It has four layers that were respectively 15,000, 10,000, 2000, and 500 ...
Munich & IWR, Heidelberg University, Germany Runway ML https://github.com/CompVis/latent-diffusion Abstract By decomposing the image formation process into a se- quential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis resul...
Modeling category-selective cortical regions with topographic variational autoencoders. arXiv https://arxiv.org/pdf/2110.13911.pdf (2021). Doshi, F. R. & Konkle, T. Visual object topographic motifs emege from self-organization of a unified representational space. bioRxiv https://doi.org/...
Configs for training a KL-regularized autoencoder on ImageNet are provided at configs/autoencoder. Training can be started by runningCUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/autoencoder/<config_spec>.yaml -t --gpus 0, ...