Papers with Code 아카이브
Domain Adaptation
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Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/feliciaxyao/icml2024-fstta
공식 코드Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation
https://github.com/felipemaiapolo/infoselect
공식 코드Effective Sample Size, Dimensionality, and Generalization in Covariate Shift Adaptation
https://github.com/fenghz/cosda
공식 코드https://github.com/fengweie/uda_clr
공식 코드Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization
https://github.com/fengxueguiren/CoPEFT
공식 코드https://github.com/fengzicai/awesomewmad
공식 코드https://github.com/fengzicai/wmad-benchmarks
공식 코드https://github.com/ff2127/bayesprompt
공식 코드https://github.com/filaPro/visda2019
공식 코드MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge
https://github.com/finnbehrendt/conditioned-diffusion-models-uad
공식 코드https://github.com/firojalam/domain-adaptation
공식 코드Domain Adaptation with Adversarial Training and Graph Embeddings
https://github.com/fiveai/lame
공식 코드https://github.com/fiy2w/cmda2022.superpolymerization
공식 코드https://github.com/fjr9516/da_nnunet
공식 코드Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation
https://github.com/flagopen/flagembedding
공식 코드Towards A Generalist Code Embedding Model Based On Massive Data Synthesis
https://github.com/floft/calda
공식 코드CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning
https://github.com/floft/codats
공식 코드Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
https://github.com/flyannie/fps
공식 코드https://github.com/fmcarlucci/ADAGE
공식 코드https://github.com/fmcarlucci/JigenDG
공식 코드https://github.com/fmlinks/domain
공식 코드Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels
https://github.com/fordevoted/uiess
공식 코드Domain Adaptation for Underwater Image Enhancement via Content and Style Separation
https://github.com/fordevoted/utilityir
공식 코드Always Clear Days: Degradation Type and Severity Aware All-In-One Adverse Weather Removal
https://github.com/foreverps/poer
공식 코드https://github.com/fpsluozi/smd-smos-proj
공식 코드https://github.com/fpv-iplab/DA-RetinaNet
공식 코드An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites
https://github.com/fpv-iplab/STMDA-RetinaNet
공식 코드https://github.com/francescocappio/oshot-meta-learning
공식 코드Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection
https://github.com/francescodisalvo05/medmnistc-api
공식 코드https://github.com/francis0625/graphical-modeling-for-multi-source-domain-adaptation
공식 코드https://github.com/frank-wang-oss/temsr
공식 코드Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation
https://github.com/frankwnb/collaborative-perception-datasets-for-autonomous-driving
공식 코드Collaborative Perception Datasets for Autonomous Driving: A Review
https://github.com/fraunhoferhhi/btseg
공식 코드BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation
https://github.com/freddieniu/prompt-qa
공식 코드https://github.com/fredzzhang/atlas
공식 코드Knowledge Composition using Task Vectors with Learned Anisotropic Scaling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.