https://github.com/anonymoustbd/mmmpt
공식 코드Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion
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/anp-scp/fast_bci
공식 코드Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface
https://github.com/anshulbshah/halp
공식 코드HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
https://github.com/ansnadeem/aic
공식 코드Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue Reports
https://github.com/anthonymlortiz/lcn
공식 코드https://github.com/anthonyzhou-1/pretraining_pdes
공식 코드https://github.com/antmachineintelligence/mtgbmcode
공식 코드MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees
https://github.com/antofuller/satvit
공식 코드Transfer Learning with Pretrained Remote Sensing Transformers
https://github.com/antongolubev5/Auto-Dataset-For-Transfer-Learning
공식 코드Transfer Learning for Improving Results on Russian Sentiment Datasets
https://github.com/aoiang/few-shot-NAS
공식 코드https://github.com/aonotas/multilingual_ASR
공식 코드Addressee and Response Selection for Multilingual Conversation
https://github.com/apexrl/DePO
공식 코드https://github.com/apexrl/depo_ngsim
공식 코드https://github.com/apivich-h/pinnacle
공식 코드PINNACLE: PINN Adaptive ColLocation and Experimental points selection
https://github.com/apmoore1/language-model
공식 코드Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification
https://github.com/apple/ml-duet
공식 코드DUET: 2D Structured and Approximately Equivariant Representations
https://github.com/apple/ml-hypersim
공식 코드Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
https://github.com/apple/ml-mobileclip
공식 코드MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
https://github.com/apple/ml-mofi
공식 코드MOFI: Learning Image Representations from Noisy Entity Annotated Images
https://github.com/apple/ml-tune-mode-convbn
공식 코드https://github.com/apple/ml-vfm-kt
공식 코드https://github.com/applied-machine-learning-lab/dt4ier
공식 코드Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
https://github.com/applied-machine-learning-lab/hi-merging
공식 코드https://github.com/applied-machine-learning-lab/moelora-peft
공식 코드When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
https://github.com/applied-machine-learning-lab/rmtl
공식 코드https://github.com/aprilsveryown/semantically-guided-continual-learning
공식 코드Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning
https://github.com/aqeelanwar/PEDRA
공식 코드https://github.com/araison12/eixgnn
공식 코드EiX-GNN : Concept-level eigencentrality explainer for graph neural networks
https://github.com/aramis-lab/AD-DL
공식 코드https://github.com/aramis-lab/clinicadl
공식 코드https://github.com/arassadin/emotiw2017
공식 코드Group-level Emotion Recognition using Transfer Learning from Face Identification
https://github.com/arcadelab/fastsam3d
공식 코드FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
https://github.com/arcaninez/odm3d
공식 코드ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection
https://github.com/arcee-ai/mergekit
공식 코드Arcee's MergeKit: A Toolkit for Merging Large Language Models
https://github.com/archiki/Robust-E2E-ASR
공식 코드An Investigation of End-to-End Models for Robust Speech Recognition
https://github.com/arefaz/e3-ensemble-of-expert-embedders-cvprwmf24
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.