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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 1426–1450 of 1854 papers

TitleStatusHype
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder—0
Make a Face: Towards Arbitrary High Fidelity Face Manipulation—0
MakeupBag: Disentangling Makeup Extraction and Application—0
Make Your Actor Talk: Generalizable and High-Fidelity Lip Sync with Motion and Appearance Disentanglement—0
Manifold Learning and Alignment with Generative Adversarial Networks—0
Manipulating Medical Image Translation with Manifold Disentanglement—0
Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder—0
MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets—0
Measuring Orthogonality in Representations of Generative Models—0
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models—0
Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation—0
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation—0
Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning—0
Metric Learning vs Classification for Disentangled Music Representation Learning—0
METTS: Multilingual Emotional Text-to-Speech by Cross-speaker and Cross-lingual Emotion Transfer—0
Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation—0
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation—0
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness—0
Mixup Barcodes: Quantifying Geometric-Topological Interactions between Point Clouds—0
MLSD-GAN -- Generating Strong High Quality Face Morphing Attacks using Latent Semantic Disentanglement—0
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation—0
Model-based occlusion disentanglement for image-to-image translation—0
Model Debiasing via Gradient-based Explanation on Representation—0
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning—0
Modular Representations for Weak Disentanglement—0
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