https://github.com/kroster/transfer-learning-pandemic-preparedness
공식 코드Forecasting new diseases in low-data settings using transfer learning
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/krystalan/awesomeseg
공식 코드Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks
https://github.com/kshitij98/multimodal-propaganda
공식 코드https://github.com/kshitijd20/RSA-CVPR19-release
공식 코드Representation Similarity Analysis for Efficient Task taxonomy & Transfer Learning
https://github.com/kuanghuei/clean-net
공식 코드CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
https://github.com/kuluhan/gtn
공식 코드Generalizing Teacher Networks for Effective Knowledge Distillation Across Student Architectures
https://github.com/kuluhan/pre-dfkd
공식 코드Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay
https://github.com/kunsaram01/DGAD
공식 코드Dynamic Guidance Adversarial Distillation with Enhanced Teacher Knowledge
https://github.com/kuntalkumarpal/t5numeracy
공식 코드Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model
https://github.com/kushal2000/Dravidian-Offensive-Language-Identification
공식 코드indicnlp@kgp at DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Languages
https://github.com/kushalchawla/CaSiNo
공식 코드CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems
https://github.com/kw2934/armul
공식 코드https://github.com/kyrie-23/linear_task_arithmetic
공식 코드Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic
https://github.com/kyrie-23/task_arithmetic_tangent
공식 코드Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic
https://github.com/lab-midas/med_segmentation
공식 코드https://github.com/labgong/dskt-ddea
공식 코드https://github.com/labull/engineeringpatternrecognition
공식 코드https://github.com/lackel/tan
공식 코드Transfer and Alignment Network for Generalized Category Discovery
https://github.com/lafite-yu/mttrans-opensource
공식 코드MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer
https://github.com/laituan245/StackExchangeQA
공식 코드https://github.com/lalithjets/domain-adaptation-in-mtl
공식 코드https://github.com/lalithjets/global-reasoned-multi-task-model
공식 코드Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding
https://github.com/lalonderodney/X-Caps
공식 코드Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses
https://github.com/lambert-x/medical_mae
공식 코드Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification
https://github.com/lambertleong/dxa-vae
공식 코드Quantitative Imaging Principles Improves Medical Image Learning
https://github.com/lamda-bbo/mcts-transfer
공식 코드Monte Carlo Tree Search based Space Transfer for Black-box Optimization
https://github.com/landskape-ai/reprogram_lt
공식 코드https://github.com/lanwuwei/GigaBERT
공식 코드An Empirical Study of Pre-trained Transformers for Arabic Information Extraction
https://github.com/laowangzi/bert4cmr
공식 코드Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from Transformer
https://github.com/laowangzi/bert4xmr
공식 코드Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from Transformer
https://github.com/laowangzi/cikgrec
공식 코드Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models
https://github.com/lbasyal/Histopathologic-Cancer-Detection-
공식 코드https://github.com/lbechberger/LearningPsychologicalSpaces
공식 코드Grounding Psychological Shape Space in Convolutional Neural Networks
https://github.com/lcmhoang/hmg-dynamics
공식 코드https://github.com/lcs2-iiitd/code-mixed-classification
공식 코드A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer
https://github.com/lct-rug-2022/edos-2023
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.