https://github.com/AIM3-RUC/ABAW4
공식 코드Multi-Task Learning Framework for Emotion Recognition in-the-wild
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/AIML-MED/AdaCBM
공식 코드AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis
https://github.com/AIMLab-UBC/MIDL2020
공식 코드https://github.com/AKASH2907/bird-species-classification
공식 코드Bird Species Classification using Transfer Learning with Multistage Training
https://github.com/AKuzina/DWP
공식 코드Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
https://github.com/ANDROComputationalSolutions/RadarCommDataset
공식 코드Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification
https://github.com/ARM-software/DeepFreeze
공식 코드FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning
https://github.com/ASAP-AEG/MTL-Essay-Traits-Scoring
공식 코드Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays
https://github.com/ATHdevs/Auto-PCOS
공식 코드Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging
https://github.com/ATOMconsortium/AMPL
공식 코드https://github.com/ATTPC/event-classification
공식 코드Machine Learning Methods for Track Classification in the AT-TPC
https://github.com/Abdullah-Abuolaim/multi-task-defocus-deblurring-dual-pixel-nimat
공식 코드Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning
https://github.com/AceSix/Knowledge_Transfer
공식 코드https://github.com/ActionEmbedding/ActionEmbedding
공식 코드https://github.com/Adapter-Hub/adapter-transformers
공식 코드AdapterFusion: Non-Destructive Task Composition for Transfer Learning
https://github.com/Aditi138/Embeddings
공식 코드https://github.com/Aditi138/EntityTargetedActiveLearning
공식 코드https://github.com/Advacheck-OU/ai-detector-coling2025
공식 코드Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking
https://github.com/AlbinSou/cross_task_cil
공식 코드On the importance of cross-task features for class-incremental learning
https://github.com/Alejandro-Galan/Few-shot_COVID-19_Inter-vs-Intra
공식 코드https://github.com/AlexMoreo/funnelling
공식 코드https://github.com/AlexMoreo/pydci
공식 코드Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments
https://github.com/AlexTheBad/AP-10K
공식 코드https://github.com/AlexandreDabySeesaram/NeuROM
공식 코드https://github.com/Alexuan/musical_instrument_embedding
공식 코드https://github.com/Algolzw/SPAC-Deformable-Registration
공식 코드RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
https://github.com/AliBeikmohammadi/TA-Explore
공식 코드Human-Inspired Framework to Accelerate Reinforcement Learning
https://github.com/Alien9427/SAR_specific_models
공식 코드What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs
https://github.com/AlifAshrafee/ViT-pytorch-for-Cooking-State-Recognition
공식 코드Rethinking Cooking State Recognition with Vision Transformers
https://github.com/AloeUoB/RSSI_GAN
공식 코드Transfer Learning of RSSI to Improve Indoor Localisation Performance
https://github.com/Alrash/TimesURL
공식 코드TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning
https://github.com/AmirooR/Pairwise-Similarity-knowledge-Transfer-WSOL
공식 코드Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
https://github.com/Amirresm/empirical_peft_suite
공식 코드https://github.com/AmmarKamoona/RFS-Energy-Anomaly-Detection-of-Defect
공식 코드https://github.com/AndReGeist/gp_squared
공식 코드Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
https://github.com/Anjin-Liu/Openset_Learning_AOSR
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.