Disentanglement
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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/TPCD/DG-VAE
공식 코드https://github.com/TeddLi/Disentangle
공식 코드DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement
https://github.com/ThomasMrY/Groupified-VAE
공식 코드Towards Building A Group-based Unsupervised Representation Disentanglement Framework
https://github.com/Tianhao-Qi/DEADiff_code
공식 코드DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations
https://github.com/TsaftarisCollaboratory/CSDisentanglement_Metrics_Library
공식 코드Measuring the Biases and Effectiveness of Content-Style Disentanglement
https://github.com/VITA-Group/UAV-NDFT
공식 코드https://github.com/Wendison/VQMIVC
공식 코드https://github.com/Willy1005/2025-IJCAI-RDML
공식 코드Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks
https://github.com/XLearning-SCU/2021-IJCV-YOLY
공식 코드You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network
https://github.com/XiangZongyi/CoPD
공식 코드Coherence-guided Preference Disentanglement for Cross-domain Recommendations
https://github.com/XiaokunSun/Barbie
공식 코드https://github.com/Xiaoming-Yu/DMIT
공식 코드Multi-mapping Image-to-Image Translation via Learning Disentanglement
https://github.com/YadiraF/DECA
공식 코드Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
https://github.com/Yang-Liu1082/FDN
공식 코드Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
https://github.com/YoungSeng/SRD-VC
공식 코드https://github.com/YuWLong666/CoE
공식 코드https://github.com/YueWuHKUST/AniFaceGAN
공식 코드AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars
https://github.com/Yuheng-Li/MixNMatch
공식 코드MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
https://github.com/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios
공식 코드https://github.com/ZZWaang/polyphonic-chord-texture-disentanglement
공식 코드Learning Interpretable Representation for Controllable Polyphonic Music Generation
https://github.com/ZejianLi/analogical-training
공식 코드Unsupervised Disentangled Representation Learning with Analogical Relations
https://github.com/ZerojumpLine/DecGAN
공식 코드Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition
https://github.com/Zhiyuan1991/proVLAE
공식 코드Progressive Learning and Disentanglement of Hierarchical Representations
https://github.com/ZoneLikeWonderland/Neural-Video-Portrait-Relighting-in-Real-time-via-Consistency-Modeling
공식 코드Neural Video Portrait Relighting in Real-time via Consistency Modeling
https://github.com/aaltoml/generative-inverse-heat-dissipation
공식 코드https://github.com/abbasiReza/CLIP-COoD
공식 코드https://github.com/aburns4/UnsupDisent
공식 코드Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions
https://github.com/adamdad/knowledgefactor
공식 코드https://github.com/agonzgarc/cross-domain-disen
공식 코드https://github.com/ahachang/defend
공식 코드Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
https://github.com/ai4life-group/sae_robustness
공식 코드https://github.com/aiandglobaldevelopmentlab/textdisentanglement
공식 코드https://github.com/akatigre/multi2one
공식 코드Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance
https://github.com/akomand/causaldiffae
공식 코드Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
https://github.com/akomand/icm-vae
공식 코드Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.