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Transfer Learning
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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/lsmcolab/semantic-segmentation-under-adverse-conditions
공식 코드https://github.com/lstrgar/self-supervised-phone-segmentation
공식 코드https://github.com/ltgoslo/fun-nom
공식 코드Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation
https://github.com/ltgoslo/multitask_negation_for_sa
공식 코드Improving Sentiment Analysis with Multi-task Learning of Negation
https://github.com/ltrottier/deep-collaboration-network
공식 코드Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection
https://github.com/ltu-machine-learning/analysis-of-deep-perceptual-loss-networks
공식 코드https://github.com/lucasalegre/sfols
공식 코드Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
https://github.com/lucascjysdl/hierairl
공식 코드Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control
https://github.com/lucascjysdl/multi-task-hierarchical-airl
공식 코드Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
https://github.com/lucashancef/e-sort
공식 코드https://github.com/lucashancef/fafesort
공식 코드https://github.com/lucaspascal/Maximum-Roaming-Mutli-Task-Learning
공식 코드https://github.com/luck1226/multisource_deepGaussianProcess
공식 코드Conditional Deep Gaussian Processes: multi-fidelity kernel learning
https://github.com/luckystarufo/MrCAE
공식 코드https://github.com/luffycodes/automated-long-answer-grading
공식 코드https://github.com/lukashedegaard/dage
공식 코드https://github.com/lukasliebel/MultiDepth
공식 코드MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
https://github.com/lukeyf/multiview_remote_sensing
공식 코드Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing
https://github.com/luogen1996/repadapter
공식 코드Towards Efficient Visual Adaption via Structural Re-parameterization
https://github.com/luohongyin/RGX
공식 코드https://github.com/luoyumei1-a/mmspyvr
공식 코드https://github.com/lupalab/Meta-Neighborhoods
공식 코드https://github.com/lutingwang/head
공식 코드HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors
https://github.com/luyaojie/text2event
공식 코드Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
https://github.com/luyongxi/deep_share
공식 코드https://github.com/lwsam/ASAP-Gaze
공식 코드https://github.com/lxchtan/ponet
공식 코드PoNet: Pooling Network for Efficient Token Mixing in Long Sequences
https://github.com/lxirich/mm-retinal
공식 코드MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining
https://github.com/lyq312318224/dream
공식 코드DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching
https://github.com/lywang3081/AFEC
공식 코드AFEC: Active Forgetting of Negative Transfer in Continual Learning
https://github.com/lzhili/fnirs-omcrd
공식 코드https://github.com/lzoltan35/duckietown_imitation_learning
공식 코드Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer
https://github.com/lzvv123456/i2cl
공식 코드https://github.com/machamp-nlp/machamp
공식 코드Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
https://github.com/machine-listeners-valencia/fsl_osr_dataset_baseline
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.