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/sherdencooper/tricks-in-deeplearning
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/shijianjian/efficientnet-pytorch-3d
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/shijing001/unicertainty_calibration_audio_classifiers
https://github.com/shin-ee-chen/bla
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/shishir-roy/decaNLP-with-BERT
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/shivam360d/Sentiment-Analysis-ULMFit
Universal Language Model Fine-tuning for Text Classification
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/shizuo-kaji/PretrainCNNwithNoData
Cats or CAT scans: transfer learning from natural or medical image source datasets?
https://github.com/shmsw25/factscore
https://github.com/shmsw25/qa-transfer
Question Answering through Transfer Learning from Large Fine-grained Supervision Data
https://github.com/shomerthesec/Research-Buildings-Energy-forecasting-using-Deep-Learning
Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data
https://github.com/shreyashankar/gpt3-sandbox
https://github.com/shrikumaran/ABInBev-Hackathon
https://github.com/shruti-jadon/Covid-19-Detection-Few-Shot-Learning
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach
https://github.com/shuishida/soaprl
DAC: The Double Actor-Critic Architecture for Learning Options
https://github.com/shujams/CT-AI-Deep-Learning-Project
https://github.com/sicara/easy-few-shot-learning
Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
https://github.com/sidak/SentEval
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/siddinc/monocular_depth_estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/sigtyp/ST2024
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/silicx/dlsa
Decoupling Representation and Classifier for Long-Tailed Recognition
https://github.com/simecek/Czech-ULMFiT
Universal Language Model Fine-tuning for Text Classification
https://github.com/simon-benigeri/narrative-generation
https://github.com/simran-arora/focus
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
https://github.com/simran-arora/privacy_fm
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
https://github.com/sithu31296/EasyFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/siyi-wind/tip
MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets
https://github.com/sjn3303123/DL_Reproducibility
https://github.com/sjtu-lit/syncse
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://github.com/sjtuzq/Cycle_Dynamics
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency
https://github.com/skhetarpal/Prostate_Cancer_Grade_Assessment
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/skojaku/Practical-Guide-to-Sentence-Transformers
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/smallbenchnlp/benchmark
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
https://github.com/smarton-empower/smarton-ai
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.