2.3 Disentanglement Learning 解纠缠学习的优势在于,除了显著的表达能力外,它还提供了一个可解释的视角来理解复杂数据表示背后的多种内在运动/因素。为了理清学习表征,最近的研究开发了β-VAE等vae来优化不同潜在因素之间的相互作用[36],[37],[38]。例如,β-VAE[36]是普通VAE的一种简单但有效的变体,它严重惩罚了...
To alleviate these limitations, we design new architectures and loss functions based on StyleGAN (Karras et al., 2019), for semi-supervised high-resolution disentanglement learning. We create two complex high-resolution synthetic datasets for systematic testing. We investigate the impact of limited ...
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations On the Transfer of Disentangled Representations in Realistic Settings 目前我对graph还不太熟悉,但是从趋势上来讲将因果推理与解耦的结合是势在必行。因为统计上的独立性来讲并不能保证因子是解耦的,或者说相关的因子...
Cheng, W., Dong, X., Khan, S., Shen, J. (2022). Learning Disentanglement with Decoupled Labels for Vision-Language Navigation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Scienc...
OPUS-DSD and cryoDRGN were used to analyze the structural heterogeneity by learning a latent space with the consensus refinement result (Fig. 6a). Fig. 6: Heterogeneity analysis of 224,354 particles of the NEXT complex. a, Starting consensus model and its gold-standard FSCs for all particles...
We hereby propose a GAN-based disentanglement learning framework called Rib Suppression GAN, or RSGAN, to perform rib suppression by utilizing the anatomical knowledge embedded in unpaired computed tomography (CT) images. In this approach, we employ a residual map to characterize the intensity ...
deep-learning reproducible-research pytorch mnist chairs-dataset vae representation-learning unsupervised-learning beta-vae celeba variational-autoencoder disentanglement dsprites fashion-mnist disentangled-representations factor-vae beta-tcvae Updated Feb 2, 2023 Python matthew...
learning FAir Representation via distributional CONtrastive Variational AutoEncoder (FarconVAE), which induces the latent space to be disentangled into sensitive and nonsensitive parts. We first construct the pair of observations with different sensitive attributes but with the same labels. Then, FarconVAE...
A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory. Following this direction, we propose in this article a novel and general paradigm for this task ...
One problem in the application of reinforcement learning to real-world problems is the curse of dimensionality on the action space. Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis. However, previous...