https://github.com/asofiaoliveira/srl_bert_pt
공식 코드Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning
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/asterisci/point-gcc
공식 코드Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast
https://github.com/astra-vision/densemtl
공식 코드DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning
https://github.com/astra-vision/stablemtl
공식 코드https://github.com/astro-informatics/leia
공식 코드https://github.com/atapour/temporal-depth-segmentation
공식 코드https://github.com/atharva20038/music4all
공식 코드Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation Models
https://github.com/athena-team/athena
공식 코드A Further Study of Unsupervised Pre-training for Transformer Based Speech Recognition
https://github.com/athms/evaluating-deeplight-transfer
공식 코드Evaluating deep transfer learning for whole-brain cognitive decoding
https://github.com/atik666/augmmentationcode
공식 코드Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data
https://github.com/atik666/representationtransfer
공식 코드Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning
https://github.com/attaullah/Self-training/blob/master/Transfer_learning.md
공식 코드Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification
https://github.com/attaullah/downsampled-plant-disease-dataset
공식 코드Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification
https://github.com/attention-is-all-i-need/mind-s-mirror-distilling-llm
공식 코드https://github.com/aub-mind/Arabic-Empathetic-Chatbot
공식 코드Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
https://github.com/audioku/meta-transfer-learning
공식 코드https://github.com/auto-icd-coding/ruccod
공식 코드https://github.com/autogluon/autogluon
공식 코드TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
https://github.com/autogluon/tabrepo
공식 코드TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
https://github.com/automl/transfer-hpo-framework
공식 코드https://github.com/autumn9999/hnps
공식 코드Episodic Multi-Task Learning with Heterogeneous Neural Processes
https://github.com/autumn9999/mtc-with-category-shifts
공식 코드Association Graph Learning for Multi-Task Classification with Category Shifts
https://github.com/autumn9999/vmtl
공식 코드https://github.com/avaapm/hatespeech
공식 코드Large-Scale Hate Speech Detection with Cross-Domain Transfer
https://github.com/avic3nna/joint-mtl-cpath
공식 코드Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
https://github.com/avivnavon/nash-mtl
공식 코드https://github.com/aws/aws-refcocog-adv
공식 코드https://github.com/awslabs/multilingual-top
공식 코드Multilingual Neural Semantic Parsing for Low-Resourced Languages
https://github.com/axedge1983/li
공식 코드https://github.com/ayaanzhaque/MultiMix
공식 코드Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data
https://github.com/ayeshaEnayet/Dialogue-Act-for-dialogue-analysis-
공식 코드A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments
https://github.com/ayu987/zero-shot-unlearning
공식 코드https://github.com/azencot-group/mtlinear
공식 코드A Multi-Task Learning Approach to Linear Multivariate Forecasting
https://github.com/b-com/anatomynlm
공식 코드https://github.com/baaivision/eva
공식 코드EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/badripatro/heracles
공식 코드Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.