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Disentanglement

This is an approach to solve a diverse set of tasks in a data efficient manner by disentangling (or isolating ) the underlying structure of the main problem into disjoint parts of its representations. This disentanglement can be done by focussing on the "transformation" properties of the world(main problem)

Papers

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Showing 231–240 of 1854 papers

TitleStatusHype
Adversarial Continual Learning for Multi-Domain Hippocampal SegmentationCode1
A Concept-Based Explainability Framework for Large Multimodal ModelsCode1
CausE: Towards Causal Knowledge Graph EmbeddingCode1
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image SegmentationCode1
Editable Free-viewpoint Video Using a Layered Neural RepresentationCode1
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical ImagingCode1
Latent Traversals in Generative Models as Potential FlowsCode1
Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modelingCode1
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object RepresentationsCode1
AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style TransferCode1
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