https://github.com/uyoung-jeong/BoIR
공식 코드BoIR: Box-Supervised Instance Representation for Multi-Person Pose Estimation
Papers with Code 아카이브
Transfer Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 6
- 보존된 평가 기록
- 10
Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/v-laurent/vrd-image-text-generator
공식 코드Spanish TrOCR: Leveraging Transfer Learning for Language Adaptation
https://github.com/vadim0x60/mimicseq
공식 코드https://github.com/valentinhofmann/geoadaptation
공식 코드https://github.com/valeoai/MTAF
공식 코드Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/MuHDi
공식 코드Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/WoodScape/tree/master/omnidet
공식 코드Surround-View Cameras based Holistic Visual Perception for Automated Driving
https://github.com/valeoai/radial
공식 코드https://github.com/valeriechen/ask-your-humans
공식 코드Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning
https://github.com/vamzimmer/multitask_seg_placenta
공식 코드https://github.com/vandadim/adas_mri
공식 코드Comparison of single and multitask learning for predicting cognitive decline based on MRI data
https://github.com/vanderschaarlab/mlforhealthlabpub
공식 코드Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/causal_multitask_gaussian_processes_ite
공식 코드Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
https://github.com/vanlinlin/auto-wcebleedgen-challenge-version-v2
공식 코드Divide and Conquer: Grounding a Bleeding Areas in Gastrointestinal Image with Two-Stage Model
https://github.com/vannguyennd/dam2p
공식 코드Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle
https://github.com/varunnair18/fish
공식 코드https://github.com/varunsingh88/MT-DeepONet
공식 코드Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving
https://github.com/vbayeslab/deepvol
공식 코드Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting
https://github.com/vcheplygina/cats-scans
공식 코드https://github.com/vcl3d/cmudrn
공식 코드Cross-Stitched Multi-task Dual Recursive Networks for Unified Single Image Deraining and Desnowing
https://github.com/veali/mlt-le
공식 코드MLT-LE: predicting drug-target binding affinity with multi-task residual neural networks
https://github.com/vectorspacelab/omnigen
공식 코드https://github.com/verenie/transfer
공식 코드The Utility of Feature Reuse: Transfer Learning in Data-Starved Regimes
https://github.com/vguptai/Neo-Nvidia-Annotations
공식 코드Progression Modelling for Online and Early Gesture Detection
https://github.com/vico-uoe/mtpsl
공식 코드Learning Multiple Dense Prediction Tasks from Partially Annotated Data
https://github.com/victorwz/tod_as_nlg
공식 코드Task-Oriented Dialogue System as Natural Language Generation
https://github.com/vid-koci/kbctransferlearning
공식 코드https://github.com/viebboy/MultilinearCompressiveLearningWithPrior
공식 코드https://github.com/viggin/DeepLesion_manual_test_set
공식 코드https://github.com/viktoraxelsen/cle-tfe
공식 코드https://github.com/vinairesearch/unibridge
공식 코드UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages
https://github.com/vinairesearch/vinmap
공식 코드Detecting Omissions in Geographic Maps through Computer Vision
https://github.com/vincen-github/DM
공식 코드https://github.com/vinh-tr-hoang/doeviapace
공식 코드Scalable method for Bayesian experimental design without integrating over posterior distribution
https://github.com/vinusankars/convolution-based-unlearnability
공식 코드https://github.com/virtualspaceman/ssl-skin-lesions
공식 코드An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.