https://github.com/tmlabonte/weakly-supervised-DETR
공식 코드Scaling Novel Object Detection with Weakly Supervised Detection Transformers
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/tmllab/2025_ICLR_IES
공식 코드https://github.com/tmllab/taidtm
공식 코드Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
https://github.com/tmlr-group/fabflex
공식 코드https://github.com/tmtuan1307/lander
공식 코드Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
https://github.com/tmtvaa/abaw4
공식 코드An Ensemble Approach for Multiple Emotion Descriptors Estimation Using Multi-task Learning
https://github.com/tobirohrer/gan-pretraining-ae
공식 코드https://github.com/tobwei/aptai
공식 코드https://github.com/toizzy/injecting-structural-hints
공식 코드Injecting structural hints: Using language models to study inductive biases in language learning
https://github.com/toizzy/tilt-transfer
공식 코드Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models
https://github.com/tolgabkm/hrplanes
공식 코드A benchmark dataset for deep learning-based airplane detection: HRPlanes
https://github.com/tom99763/Contrastive-Bi-Projector-for-Unsupervised-Domain-Adaption
공식 코드https://github.com/tomjz/online-knode-mpc
공식 코드Online Dynamics Learning for Predictive Control with an Application to Aerial Robots
https://github.com/tommasomarzi/fgrl
공식 코드https://github.com/tomsheng21/adaptguard
공식 코드AdaptGuard: Defending Against Universal Attacks for Model Adaptation
https://github.com/tonyby/m3
공식 코드https://github.com/tonyby/ramqa
공식 코드RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering
https://github.com/tonyhuang2022/upl
공식 코드https://github.com/torbensdjohansen/hana
공식 코드HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition
https://github.com/torchjd/torchjd
공식 코드https://github.com/tournesol-saturday/rail
공식 코드RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT
https://github.com/tpark94/spnv2
공식 코드Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap
https://github.com/trae1oung/neural_incompatibility
공식 코드https://github.com/transbiozi/mtlcomb
공식 코드https://github.com/transducens/mtl-da-emnlp
공식 코드https://github.com/transfer-learning-asr/transfer-learning-asr
공식 코드https://github.com/translatorswb/TWB-MT
공식 코드Tigrinya Neural Machine Translation with Transfer Learning for Humanitarian Response
https://github.com/tristandeleu/synergies-disentanglement-sparsity
공식 코드https://github.com/tshi04/DMSC_FEDA
공식 코드An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis
https://github.com/tsinghua-fib-lab/gpd
공식 코드Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
https://github.com/tsinghua-fib-lab/recsys2023-sine
공식 코드Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
https://github.com/tsinghua-msiip/uni-med
공식 코드Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
https://github.com/tsingz0/dbe
공식 코드Eliminating Domain Bias for Federated Learning in Representation Space
https://github.com/tsingz0/fedktl
공식 코드https://github.com/tsingz0/htfllib
공식 코드HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark
https://github.com/tstandley/taskgrouping
공식 코드Which Tasks Should Be Learned Together in Multi-task Learning?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.