https://github.com/ritatsousa/expressionKGplus
공식 코드Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs
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/ritikajha/Attribute-prediction-in-masked-facial-images-with-deep-multitask-learning
공식 코드MaskMTL: Attribute prediction in masked facial images with deep multitask learning
https://github.com/rizar/CLOSURE
공식 코드CLOSURE: Assessing Systematic Generalization of CLEVR Models
https://github.com/rizwan09/NLPDV
공식 코드https://github.com/rizwanqureshi123/brain-tumor-synthetic-data
공식 코드Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
https://github.com/rkobler/TSMNet
공식 코드SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
https://github.com/rktamplayo/LeTraNets
공식 코드https://github.com/rkwitt/GuidedAugmentation
공식 코드https://github.com/rl4m/med-peft
공식 코드Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models
https://github.com/rlworkgroup/metaworld
공식 코드Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
https://github.com/rmit-ir/walert
공식 코드https://github.com/rmusab/vul-llm-finetune
공식 코드Finetuning Large Language Models for Vulnerability Detection
https://github.com/rnjtsh/graphical-object-detector
공식 코드https://github.com/robinlu1209/st-gfsl
공식 코드Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
https://github.com/robocin/rSoccer
공식 코드rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer
https://github.com/robot-perception-group/adaptive_agent
공식 코드Task and Domain Adaptive Reinforcement Learning for Robot Control
https://github.com/robotic-vision-lab/few-shot-fruit-segmentation-via-transfer-learning
공식 코드https://github.com/robotics-upo/og-sgg
공식 코드https://github.com/robotsgomental/gtl_poc
공식 코드https://github.com/rocccyan/dgpm
공식 코드Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery
https://github.com/rohitash-chandra/CMTL_dynamictimeseries
공식 코드Co-evolutionary multi-task learning for dynamic time series prediction
https://github.com/rolsheng/mm-vufm4ds
공식 코드https://github.com/ronggong/musical-onset-efficient
공식 코드Towards an efficient deep learning model for musical onset detection
https://github.com/rotmanguy/mtal
공식 코드Multi-task Active Learning for Pre-trained Transformer-based Models
https://github.com/royabel/top-down-networks
공식 코드Biologically-Motivated Learning Model for Instructed Visual Processing
https://github.com/royalskye/routing-mvmoe
공식 코드MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
https://github.com/roydenwa/cell-centroid-former
공식 코드CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection
https://github.com/roydenwa/efficient-cell-seg
공식 코드EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring
https://github.com/royhirsch/endossl
공식 코드https://github.com/rperi/trustworthy-asv-fairness
공식 코드https://github.com/rpidial/disease-informed-vlm-adaptation
공식 코드https://github.com/rrahmati/roboinstruct-2
공식 코드https://github.com/rrr-uom-projects/contour_auto_qatool
공식 코드Automatic identification of segmentation errors for radiotherapy using geometric learning
https://github.com/rrsguim/phd_economics
공식 코드https://github.com/rsedgwick/tl_doe_4_dna
공식 코드https://github.com/rsedgwick/tlgps
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.