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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 911–920 of 1854 papers

TitleStatusHype
DreamLight: Towards Harmonious and Consistent Image Relighting—0
Controllable Data Generation Via Iterative Data-Property Mutual Mappings—0
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition—0
Impact of Disentanglement on Pruning Neural Networks—0
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology—0
Improved Disentanglement through Aggregated Convolutional Feature Maps—0
Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps—0
Improved Neural Text Attribute Transfer with Non-parallel Data—0
Contrastive Speaker Embedding With Sequential Disentanglement—0
Audio-driven High-resolution Seamless Talking Head Video Editing via StyleGAN—0
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