https://github.com/migalkin/SQuAD-es-mt
공식 코드El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks
Papers with Code 아카이브
Transfer Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 6
- 보존된 평가 기록
- 10
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/mihawkhu/asc_knowledge_transfer
공식 코드A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer
https://github.com/mihir3009/in-boxbart
공식 코드In-BoXBART: Get Instructions into Biomedical Multi-Task Learning
https://github.com/mikahama/murre
공식 코드Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity
https://github.com/mikecovlee/mLoRA
공식 코드MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
https://github.com/mikehuisman/transfer-meta-feature-representations
공식 코드Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques
https://github.com/mikelu-shanghai/CrackIdentificationEvaluation-via-DeepLearning
공식 코드https://github.com/mikelzc1990/nsganetv2
공식 코드NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search
https://github.com/mikewangwzhl/multitask-finetuning_clip
공식 코드Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning
https://github.com/mikomel/sal
공식 코드One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
https://github.com/mikuhatsune/wsod_transfer
공식 코드Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
https://github.com/milab-pku/dcpt
공식 코드Universal Image Restoration Pre-training via Degradation Classification
https://github.com/miladramzy/sequentialmetatransferpinns
공식 코드https://github.com/miladvazan/Joint_Learning_Classification_Persian_Reviews-
공식 코드https://github.com/miladvazan/convolutional-neural-networks-based-multi-task-ensemble-model
공식 코드https://github.com/mimbres/yourmt3
공식 코드https://github.com/mims-harvard/raincoat
공식 코드Domain Adaptation for Time Series Under Feature and Label Shifts
https://github.com/mingjiewang0606/fedcl_pubic
공식 코드FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity
https://github.com/mingjiewen/general-nnp-model-for-c-h-n-o-energetic-materials
공식 코드A General Neural Network Potential for Energetic Materials with C, H, N, and O elements
https://github.com/mingqianghan/PalmFruitMaturity-CNN
공식 코드Deep Convolutional Neural Networks for Palm Fruit Maturity Classification
https://github.com/mingshiyan/crgcn
공식 코드Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
https://github.com/minimario/morbit
공식 코드Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning
https://github.com/minivision-ai/photo2cartoon
공식 코드Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/minjiyoon/ktn
공식 코드Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks
https://github.com/minnesotanlp/p2c
공식 코드Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning
https://github.com/minoru938/cspml
공식 코드Shotgun crystal structure prediction using machine-learned formation energies
https://github.com/mipunivie/deeperwin
공식 코드https://github.com/miriamcobo/multitasklearning_ich_prognosis
공식 코드Multi-task Learning Approach for Intracranial Hemorrhage Prognosis
https://github.com/miriamschirmer/genocide-transcript-corpus
공식 코드A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts
https://github.com/misahub2023/benchmarking-codes-of-the-wcebleedgen-dataset
공식 코드https://github.com/mistralai/mistral-src
공식 코드https://github.com/mit-gfx/ContinuousParetoMTL
공식 코드Efficient Continuous Pareto Exploration in Multi-Task Learning
https://github.com/mit-han-lab/offsite-tuning
공식 코드https://github.com/mit-han-lab/tinyML
공식 코드TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning
https://github.com/mit-ll/robust-vision-language-finetuning
공식 코드Robust Fine-Tuning of Vision-Language Models for Domain Generalization
https://github.com/mitchellgordon95/bert-prune
공식 코드Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.