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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/simonecantori/supervised-learning-of-quantum-circuits
공식 코드Supervised learning of random quantum circuits via scalable neural networks
https://github.com/simonguiroy/abe
공식 코드Improving Meta-Learning Generalization with Activation-Based Early-Stopping
https://github.com/simula-complex/evoclinical
공식 코드https://github.com/sinzlab/neural_cotraining
공식 코드Towards robust vision by multi-task learning on monkey visual cortex
https://github.com/sionan/miccai2024-restl
공식 코드https://github.com/siyi-wind/mdvit
공식 코드MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets
https://github.com/siyiwu0330/SDF-TopoNet
공식 코드https://github.com/sjtu-compling/mela
공식 코드https://github.com/sjtu-mxtao/semantic-communication-systems
공식 코드Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data
https://github.com/sjunhongshen/unifiedpdesolvers
공식 코드UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation
https://github.com/sjyucnel/pri-graphs
공식 코드https://github.com/skhaki18/optin-transformer-pruning
공식 코드https://github.com/skoltech-nlp/formality
공식 코드Detecting Text Formality: A Study of Text Classification Approaches
https://github.com/skydustz/aec-domain-corpora
공식 코드Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain
https://github.com/skydustz/text-classification-based-approach-for-evaluating-and-enhancing-machine-interpretability-of-building
공식 코드https://github.com/skylorh/FedCLASS
공식 코드Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
https://github.com/skyorca/awesome-graph-domain-adaptation-papers
공식 코드https://github.com/skyorca/bottrans
공식 코드BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
https://github.com/slimgroup/Software.SEG2020
공식 코드Transfer learning in large-scale ocean bottom seismic wavefield reconstruction
https://github.com/slimm-lab/pyprnn
공식 코드https://github.com/slp-rl/slamkit
공식 코드https://github.com/smallmax00/counting_with_adaptive_auxiliary
공식 코드https://github.com/smeznar/Epidemic-spreading-CN2020
공식 코드Transfer Learning for Node Regression Applied to Spreading Prediction
https://github.com/smjtgupta/condmtl
공식 코드https://github.com/snap-research/r2l
공식 코드https://github.com/snap-stanford/autotransfer
공식 코드AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks
https://github.com/snap-stanford/graphgym
공식 코드Relational Multi-Task Learning: Modeling Relations between Data and Tasks
https://github.com/snowood1/zero-shot-plover
공식 코드Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification
https://github.com/snowyj/sem_syn_separation
공식 코드Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders
https://github.com/snu-mllab/context-memory
공식 코드Compressed Context Memory For Online Language Model Interaction
https://github.com/snudatalab/AugWard
공식 코드AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
https://github.com/soCzech/MultiTaskObjectStates
공식 코드Multi-Task Learning of Object State Changes from Uncurated Videos
https://github.com/soanagno/wakenet
공식 코드https://github.com/social-ai-studio/crossmodaltransferlearning
공식 코드Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video Detection
https://github.com/socialmediaie/MTML_HateSpeech
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.