https://github.com/quarxilon/generator_attribution
공식 코드Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated Images
Papers with Code 아카이브
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/qzhb/srl
공식 코드Self-Regulated Learning for Egocentric Video Activity Anticipation
https://github.com/r9y9/wavenet_vocoder
공식 코드https://github.com/rabeehk/compacter
공식 코드Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
https://github.com/rabeehk/hyperformer
공식 코드Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
https://github.com/rabeehk/vibert
공식 코드Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
https://github.com/racai-ai/romanian-distilbert
공식 코드Distilling the Knowledge of Romanian BERTs Using Multiple Teachers
https://github.com/rachel-1/qa_plausibility
공식 코드Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning
https://github.com/rachneet/cross-task-figurative-explanations
공식 코드Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5
https://github.com/ragavvenkatesan/generality
공식 코드https://github.com/raghul-parthipan/dont_waste_data
공식 코드Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation
https://github.com/rahimentezari/datadistributiontransferlearning
공식 코드https://github.com/rahul-t-p/ASVspoof-2019
공식 코드Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer Learning
https://github.com/rahul13ramesh/deep_reference_priors
공식 코드Deep Reference Priors: What is the best way to pretrain a model?
https://github.com/rahular/ellipsis-baselines
공식 코드https://github.com/rahulsmehta/sparsity-experiments
공식 코드https://github.com/rail-berkeley/BridgeData-V2
공식 코드https://github.com/rain-axt/growing-neural-networks
공식 코드https://github.com/rainyugg/blip-adapter
공식 코드BLIP-Adapter: Parameter-Efficient Transfer Learning for Mobile Screenshot Captioning
https://github.com/raipranav/fcl-fedseit
공식 코드Federated Continual Learning for Text Classification via Selective Inter-client Transfer
https://github.com/raivnlab/neural-priming
공식 코드https://github.com/rajammanabrolu/KG-DQN
공식 코드Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning
https://github.com/rajdeep345/mtlts
공식 코드MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
https://github.com/rajeevyasarla/Syn2Real
공식 코드Syn2Real Transfer Learning for Image Deraining using Gaussian Processes
https://github.com/ramakanth-pasunuru/CAS-MAS
공식 코드https://github.com/ramtin-mojtahedi/ovtps
공식 코드Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation
https://github.com/rango-zhang-hang/ntu-visual-recognition-via-vision-language-model-transfer
공식 코드M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object Detection
https://github.com/raoofnaushad/eurosat_lulc
공식 코드Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study
https://github.com/raoulushpc/deep_residual_voxel_autoencoder
공식 코드Simplified Learning of CAD Features Leveraging a Deep Residual Autoencoder
https://github.com/raoyongming/GFNet
공식 코드https://github.com/raphael-baena/FIERCE-repo
공식 코드Preserving Fine-Grain Feature Information in Classification via Entropic Regularization
https://github.com/raquelaoki/m3e2
공식 코드https://github.com/rasilab/github_demo
공식 코드GitHub is an effective platform for collaborative and reproducible laboratory research
https://github.com/rasilab/github_template
공식 코드GitHub is an effective platform for collaborative and reproducible laboratory research
https://github.com/raumannsr/ENHANCE
공식 코드https://github.com/raumannsr/hints_crowd
공식 코드Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.