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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/smwanghhh/co-sa

공식 코드

Cooperative Sentiment Agents for Multimodal Sentiment Analysis

https://github.com/snu-mllab/DisentanglementICML19

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Learning Discrete and Continuous Factors of Data via Alternating Disentanglement

https://github.com/songrise/Artist

공식 코드

DiffArtist: Towards Structure and Appearance Controllable Image Stylization

https://github.com/songrise/mope

공식 코드

MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion

https://github.com/songruiecho/tacit

공식 코드

TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification

https://github.com/soon-yau/upgpt

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UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer

https://github.com/soonwook34/crkt

공식 코드

Enhancing Knowledge Tracing with Concept Map and Response Disentanglement

https://github.com/sooyyoungg/aesfa

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AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

https://github.com/spengliang/did-m3d

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DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection

https://github.com/stanfordmlgroup/disentanglement

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Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

https://github.com/starbucks-drinker/mi-segnet

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MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization

https://github.com/stathismegas/celcomen

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Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling

https://github.com/stathius/sd-vae

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Disentangled Generative Models for Robust Prediction of System Dynamics

https://github.com/stelabou/stylegan_directions_face_reenactment

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Finding Directions in GAN's Latent Space for Neural Face Reenactment

https://github.com/stelabou/stylemask

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StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment

https://github.com/stormraiser/disunknown

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DisUnknown: Distilling Unknown Factors for Disentanglement Learning

https://github.com/subbareddy248/mllm_videos

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Instruction-Tuned Video-Audio Models Elucidate Functional Specialization in the Brain

https://github.com/supershiye/UniformTransformationModule

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Uniform Transformation: Refining Latent Representation in Variational Autoencoders

https://github.com/sureli/dicyr_code

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Disentanglement by Cyclic Reconstruction

https://github.com/swook/faze_preprocess

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Self-Learning Transformations for Improving Gaze and Head Redirection

https://github.com/tangtao-pku/arts

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ARTS: Semi-Analytical Regressor using Disentangled Skeletal Representations for Human Mesh Recovery from Videos

https://github.com/tanodino/sshda

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Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling

https://github.com/tanpei0513/vicsek_trajectory

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Intrinsic statistical separation of subpopulations in heterogeneous collective motion via dimensionality reduction

https://github.com/tbepler/spatial-VAE

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Explicitly disentangling image content from translation and rotation with spatial-VAE

https://github.com/theamaya/3dlatnav

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3DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulation

https://github.com/thefonseca/factorsum

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Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents

https://github.com/thesstefan/pixlens

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PixLens: A Novel Framework for Disentangled Evaluation in Diffusion-Based Image Editing with Object Detection + SAM

https://github.com/thomasmry/disdiff

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DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

https://github.com/tianxiangzhao/imitationnoisydemon

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Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

https://github.com/tleemann/identifiable_concepts

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When are Post-hoc Conceptual Explanations Identifiable?

https://github.com/tomtom1103/compose-and-conquer

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Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis

https://github.com/travers-rhodes/jlonevae

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Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization

https://github.com/tristandeleu/synergies-disentanglement-sparsity

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Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning

https://github.com/truongvu2000nd/afs

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Face Swapping as A Simple Arithmetic Operation

https://github.com/trustworthy-ai-uu-nki/cross-modal-segmentation

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Enhancing Cross-Modal Medical Image Segmentation through Compositionality

https://github.com/trustworthy-ai-uu-nki/federated-learning-disentanglement

공식 코드

FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation

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