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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 1661–1670 of 1854 papers

TitleStatusHype
Image Sentiment Transfer—0
Image Style Transfer and Content-Style Disentanglement—0
Impact of Disentanglement on Pruning Neural Networks—0
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder—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
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT—0
Improving Editability in Image Generation with Layer-wise Memory—0
Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models—0
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