https://github.com/dehbaiyor/idlfall2021project
공식 코드Extreme Multi-Domain, Multi-Task Learning With Unified Text-to-Text Transfer Transformers
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/delta-papa/Robopol-artifacts
공식 코드Eliminating artefacts in Polarimetric Images using Deep Learning
https://github.com/demolakstate/AdeNet-Deep-Learning-Architecture
공식 코드AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines
https://github.com/demolakstate/eGAN_addressing_class_imbalance_with_transfer_learning_on_GAN
공식 코드eGAN: Unsupervised approach to class imbalance using transfer learning
https://github.com/dengyang17/MTQA
공식 코드https://github.com/dengyang17/pacific
공식 코드https://github.com/dengyang17/unimind
공식 코드A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
https://github.com/dengyh16code/deformable-gnn
공식 코드Learning visual-based deformable object rearrangement with local graph neural networks
https://github.com/densechen/AReLU
공식 코드https://github.com/deqingyang/mgbr
공식 코드Group Buying Recommendation Model Based on Multi-task Learning
https://github.com/derekabc/cottonweeds
공식 코드https://github.com/derekdijin/adamel-supplementary
공식 코드Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation
https://github.com/derekhoward/Reachout_triage
공식 코드Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study
https://github.com/destiny301/pmfl
공식 코드https://github.com/detectivemb/task-arithmetic-for-zs-ir
공식 코드Investigating Task Arithmetic for Zero-Shot Information Retrieval
https://github.com/devanshg27/sem-tab-fact
공식 코드https://github.com/devavrattomar/sst
공식 코드Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
https://github.com/devichand579/HPT
공식 코드https://github.com/dewenzeng/cl-tci
공식 코드https://github.com/dewenzeng/clsp
공식 코드https://github.com/dewenzeng/positional_cl
공식 코드Positional Contrastive Learning for Volumetric Medical Image Segmentation
https://github.com/dfki-nlp/mobie
공식 코드https://github.com/dfki-nlp/multitacred
공식 코드MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset
https://github.com/dgiova/bert-lm-transferability
공식 코드https://github.com/dguo98/DiffPruning
공식 코드https://github.com/diadai/cfs
공식 코드Open Continual Feature Selection via Granular-Ball Knowledge Transfer
https://github.com/diagnijmegen/medicaltransferlearning3d-unet
공식 코드Transfer learning from a sparsely annotated dataset of 3D medical images
https://github.com/diamondfan/fairseq
공식 코드Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR
https://github.com/dichao-liu/lightweight_distracted_driver_recognition_with_distillation-based_nas_and_knowledge_transfer
공식 코드https://github.com/didi/hetsann
공식 코드An Attention-based Graph Neural Network for Heterogeneous Structural Learning
https://github.com/didzis/tensorflowAMR
공식 코드Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
https://github.com/dilab-ustcsz/cmust
공식 코드Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
https://github.com/diliadis/deepmtp
공식 코드https://github.com/dilis-lab/simbaml_examples
공식 코드SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data
https://github.com/dillonreis/real-time-flying-object-detection_with_yolov8
공식 코드https://github.com/dimarziobian/cpmr
공식 코드CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.