https://github.com/JDAI-CV/faceX-Zoo
공식 코드Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning
Papers with Code 아카이브
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/JacopoDapueto/transfer_disentanglement
공식 코드Transferring disentangled representations: bridging the gap between synthetic and real images
https://github.com/JanineCHEN/SE-VGAE
공식 코드https://github.com/JasonForJoy/SA-BERT
공식 코드Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
https://github.com/JeremDona/spatiotemporal_variable_separation
공식 코드https://github.com/Jia1018/GoHD
공식 코드https://github.com/JiayuSuPKU/sispca
공식 코드https://github.com/Kaiseem/AD-GAN
공식 코드AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training
https://github.com/KamilDeja/multiband_vae
공식 코드Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
https://github.com/LIAvignon/adversarial-disentangling-autoencoder-for-spk-representation
공식 코드Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation
https://github.com/LabyrinthineLeo/DISCO
공식 코드https://github.com/Liao-Xu/DDR
공식 코드Deep Dimension Reduction for Supervised Representation Learning
https://github.com/MECLabTUDA/ACS
공식 코드Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
https://github.com/Meowuu7/equi-articulated-pose
공식 코드Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance
https://github.com/MingyuY/deformable-view-synthesis
공식 코드Novel View Synthesis on Unpaired Data by Conditional Deformable Variational Auto-Encoder
https://github.com/Morpheus3000/ShadingNet
공식 코드ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition
https://github.com/NIEQiang001/unsupervised-human-pose
공식 코드Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement
https://github.com/NVlabs/CoordGAN
공식 코드CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs
https://github.com/NVlabs/ffhq-dataset
공식 코드A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/NVlabs/stylegan
공식 코드A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/OllieBoyne/FIND
공식 코드FIND: An Unsupervised Implicit 3D Model of Articulated Human Feet
https://github.com/OmerKafri/StyleFusion
공식 코드StyleFusion: A Generative Model for Disentangling Spatial Segments
https://github.com/PRIS-CV/Fine-Grained-or-Not
공식 코드https://github.com/ParitoshParmar/Fitness-AQA
공식 코드Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
https://github.com/ParthaEth/GIF
공식 코드https://github.com/Pei-KaiHuang/AAAI25-SLIP
공식 코드SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
https://github.com/PrototypeNx/DETEX
공식 코드Decoupled Textual Embeddings for Customized Image Generation
https://github.com/Purdue-M2/AI-Synthesized-Voice-Generalization
공식 코드https://github.com/QingyuZhao/VAE-for-Regression
공식 코드Variational AutoEncoder For Regression: Application to Brain Aging Analysis
https://github.com/QualityAssessment/DOVER
공식 코드https://github.com/RandallBalestriero/GAN
공식 코드https://github.com/RetroCirce/Auto-mask-Music-Generative-Model-via-EC2-VAE-Disentanglement
공식 코드Continuous Melody Generation via Disentangled Short-Term Representations and Structural Conditions
https://github.com/SUSTechBruce/Med-UniC
공식 코드Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
https://github.com/SkafteNicki/unsuper
공식 코드Explicit Disentanglement of Appearance and Perspective in Generative Models
https://github.com/SsGood/ADGCN
공식 코드https://github.com/Strong-AI-Lab/ct-vae
공식 코드Disentanglement of Latent Representations via Causal Interventions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.