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/DaoD/KPN
공식 코드Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals
https://github.com/DaphneAntotsiou/MAPS
공식 코드Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division
https://github.com/Darel13712/pretrained-audio-representations
공식 코드Comparative Analysis of Pretrained Audio Representations in Music Recommender Systems
https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG
공식 코드https://github.com/DavideBuffelli/TrASenD
공식 코드Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
https://github.com/DebeshJha/2020-CBMS-DoubleU-Net
공식 코드DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
https://github.com/DebeshJha/NanoNet
공식 코드NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
https://github.com/DeepLabCut/DeepLabCut
공식 코드Markerless tracking of user-defined features with deep learning
https://github.com/DeepSpectrum/DeepSpectrumLite
공식 코드https://github.com/DefangChen/OKDDip-AAAI2020
공식 코드https://github.com/DefangChen/SemCKD
공식 코드https://github.com/DevashishPrasad/CascadeTabNet
공식 코드https://github.com/DianDYu/MIDAS_dialog_act
공식 코드MIDAS: A Dialog Act Annotation Scheme for Open Domain Human Machine Spoken Conversations
https://github.com/DianDYu/language_embeddings
공식 코드Language Embeddings for Typology and Cross-lingual Transfer Learning
https://github.com/Dilruk/CnGAN
공식 코드https://github.com/Dingkun0817/SDDA
공식 코드Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
https://github.com/DirtyHarryLYL/HAKE
공식 코드https://github.com/DirtyHarryLYL/HAKE-Action-Torch
공식 코드https://github.com/Dong-Jie-Chen/RCL
공식 코드Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning
https://github.com/DongSky/lbba_boosted_wsod
공식 코드Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
https://github.com/DongyoungLim/TUSLA_RELU
공식 코드https://github.com/Dreem-Organization/RobustSleepNet
공식 코드RobustSleepNet: Transfer learning for automated sleep staging at scale
https://github.com/DylanOrange/geal
공식 코드GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency
https://github.com/EIDOSlab/PSP-entropy
공식 코드Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
https://github.com/EIDOSlab/unveiling-covid19-from-cxr
공식 코드Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
https://github.com/EIHW/EmoNet
공식 코드EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition
https://github.com/EPFL-VILAB/XTConsistency
공식 코드https://github.com/EddCBen/Classifying-Textual-Data-with-pretrained-Vision-Models-through-Transfer-Learning-and-Data-Transforms
공식 코드https://github.com/EdisonNi-hku/when-does-mtl-work
공식 코드When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP
https://github.com/Edresson/TTS-Portuguese-Corpus
공식 코드TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese
https://github.com/EducationalTestingService/CATS
공식 코드Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation
https://github.com/Efstathia-Soufleri/PKD
공식 코드Advancing Compressed Video Action Recognition through Progressive Knowledge Distillation
https://github.com/EiraZhang/LACO
공식 코드Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning
https://github.com/ElementAI/seasonal-contrast
공식 코드Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
https://github.com/Elhoseiny-VisionCAIR-Lab/CIZSL.v2
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.