The method also includes performing one or more denoising operations over the score-based generative model to convert the first set of values into a first set of latent variable values associated with a latent space. The method further includes converting the first set of latent variable values ...
Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space, relying on the variational autoencoder framework. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and...
Score- based generative modeling through stochastic differential equations. In International Conference on Learning Repre- sentations, 2021. 2, 3, 15 [83] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions cl...
3.1. Data Processing and Annotation 10 11.09 8 6 4 2.65 2 1e7 8 6 4 2 0 Raw Processed 0 0.00 0.25 0.50 0.75 1.00 1.25 1.50 Data Optical Flow Score Figure 2. Our initial dataset contains many static scenes and cuts which hurts training of generative video models. Le...
LSGM trains a score-based generative model (a.k.a. a denoising diffusion model) in the latent space of a variational autoencoder. It currently achieves state-of-the-art generative performance on several image datasets.RequirementsLSGM is built in Python 3.8 using PyTorch 1.8.0. Please use the...
Vahdat, A., Kreis, K., Kautz, J.: Score-based generative modeling in latent space. Adv. Neural Inf. Process. Syst. 34, 11287–11302 (2021) Google Scholar Wu, J., Zhang, C., Xue, T., et al.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial mo...
5(a), we performed inference five times for a speaker with the same musical score. It can be observed that HiddenSinger synthesized singing voices that contained appropriate tunes based on the musical score and variations such as intonation. As indicated in Fig. 5(b), we synthesized singing ...
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., & Poole, B. (2021). Score-based generative modeling through stochastic differential equations. InICLR Soomro, K., Zamir, A.R., & Shah, M. (2012). Ucf101: A dataset of 101 human actions classes from videos...
Score-based generative modeling through stochastic differential equa- tions. arXiv preprint arXiv:2011.13456, 2020. 2, 7 [64] Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J. Engel, Raul Mur-Artal, Carl Ren, Shobhit...
While approaches to jointly learn an encoding/decoding model together with a score-based prior exist [90], they still require a difficult weighting between reconstruction and generative capabil- ities [11] and are outperformed by our approach (Sec. 4). 3. Method To lo...