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Disentanglement

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평가표: 3
보존된 평가 기록: 4

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)

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hlzhang109/ddg

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Towards Principled Disentanglement for Domain Generalization

https://github.com/hmohebbi/disentangling_representations

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Disentangling Textual and Acoustic Features of Neural Speech Representations

https://github.com/hollerm/generator_based_motion_separation

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Latent-space disentanglement with untrained generator networks for the isolation of different motion types in video data

https://github.com/hongyehu/RG-Flow

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RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior

https://github.com/hs-oh-prml/durflexevc

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DurFlex-EVC: Duration-Flexible Emotional Voice Conversion Leveraging Discrete Representations without Text Alignment

https://github.com/huaxiuyao/TALLY

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Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations

https://github.com/hujiecpp/NAD

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Architecture Disentanglement for Deep Neural Networks

https://github.com/hzzone/adatrans

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Adaptive Nonlinear Latent Transformation for Conditional Face Editing

https://github.com/i2-multimedia-lab/magnet

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Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function

https://github.com/iamkanghyunchoi/qimera

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Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

https://github.com/ibm/in-memory-factorizer

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In-memory factorization of holographic perceptual representations

https://github.com/ictmcg/u-vap

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U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation

https://github.com/iffsid/disentangling-disentanglement

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Disentangling Disentanglement in Variational Autoencoders

https://github.com/ignisavium/causal-llava

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Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models

https://github.com/iiscleap/zest

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Zero Shot Audio to Audio Emotion Transfer With Speaker Disentanglement

https://github.com/illumara/vton-handfit

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VTON-HandFit: Virtual Try-on for Arbitrary Hand Pose Guided by Hand Priors Embedding

https://github.com/ilyatrofimov/rtd

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Representation Topology Divergence: A Method for Comparing Neural Network Representations

https://github.com/imalne/pbasr

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A New Dataset and Framework for Real-World Blurred Images Super-Resolution

https://github.com/imlixinyang/AGUIT

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Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning

https://github.com/imlixinyang/HiSD

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Image-to-image Translation via Hierarchical Style Disentanglement

https://github.com/insitro/sams-vae

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Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder

https://github.com/iremcetin/attri-vae

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Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders

https://github.com/isno0907/localbasis

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Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs

https://github.com/ituvisionlab/factorqvae

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Disentanglement with Factor Quantized Variational Autoencoders

https://github.com/iurada/talos-task-arithmetic

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Efficient Model Editing with Task-Localized Sparse Fine-tuning

https://github.com/ivrl/lelsd

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Optimizing Latent Space Directions For GAN-based Local Image Editing

https://github.com/j-x-chen/ggavatar

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GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars from Monocular Video

https://github.com/jackbrady/interaction-asymmetry

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Interaction Asymmetry: A General Principle for Learning Composable Abstractions

https://github.com/jadgardner/neusky

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The Sky's the Limit: Re-lightable Outdoor Scenes via a Sky-pixel Constrained Illumination Prior and Outside-In Visibility

https://github.com/jaeill/CVPR23-VNE

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VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution

https://github.com/jaekyeom/IBOL

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Unsupervised Skill Discovery with Bottleneck Option Learning

https://github.com/jakaria08/explaining_shape_variability

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Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning

https://github.com/jakobabesser/icassp_2022_disentanglementasc

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Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

https://github.com/jankoune/dpi-vae

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Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder

https://github.com/jgkwak95/AU-GAN

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Adverse Weather Image Translation with Asymmetric and Uncertainty-aware GAN

https://github.com/jhtonykoo/music_mixing_style_transfer

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Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects

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