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Found 6 papers, 2 papers with code
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use of neural networks.
Disentangled State Space Representations
Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist.
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning.
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class.
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation
In clinical practice, regions of interest in medical imaging often need to be identified through a process of precise image segmentation.
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.
