https://github.com/PaddlePaddle/ERNIE
공식 코드ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
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/PaddlePaddle/PaddleClas
공식 코드https://github.com/PaddlePaddle/PaddleHelix
공식 코드https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-tiny
공식 코드ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization
https://github.com/PaoloBonettiPolimi/NonLinCTFA
공식 코드Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis
https://github.com/Paranioar/DBL
공식 코드Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching
https://github.com/Paranioar/UniPT
공식 코드UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
https://github.com/ParitoshParmar/HalluciNet
공식 코드HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/ParitoshParmar/MTL-AQA
공식 코드What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/PatrickZH/A-Large-scale-Attribute-Dataset-for-Zero-shot-Learning
공식 코드https://github.com/PedroRASB/COVID-19-Twice-Transfer-DNNs
공식 코드A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays
https://github.com/Peihao-Xiang/MTCAE-DFER
공식 코드MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition
https://github.com/PengchaoHan/RDSKD
공식 코드Robustness and Diversity Seeking Data-Free Knowledge Distillation
https://github.com/Pengxin-Guo/HGNN
공식 코드Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
https://github.com/PengyiZhang/MIADeepSSL
공식 코드A Survey on Deep Learning of Small Sample in Biomedical Image Analysis
https://github.com/PericlesHat/enhanced-GPDM
공식 코드https://github.com/PerryXDeng/weaponizing_unicode
공식 코드Weaponizing Unicodes with Deep Learning -- Identifying Homoglyphs with Weakly Labeled Data
https://github.com/PeterouZh/CIPS-3D
공식 코드CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
https://github.com/PhoebusSi/CTIR
공식 코드Learning Class-Transductive Intent Representations for Zero-shot Intent Detection
https://github.com/Pliploop/SemiSupCon
공식 코드Semi-Supervised Contrastive Learning of Musical Representations
https://github.com/Pongpisit-Thanasutives/Physics-Informed-Neural-Networks-Multitask-Learning
공식 코드https://github.com/PramuPerera/TransferLearningNovelty
공식 코드https://github.com/Prasath2001/Party-Symbol-Classifier
공식 코드https://github.com/Prof-Pratik-Shah-Lab-UCI/skin_project_2024
공식 코드https://github.com/Prog-Jacob/watermarking-eeg-models
공식 코드Protecting Intellectual Property of EEG-based Neural Networks with Watermarking
https://github.com/Psarpei/Multi-Type-TD-TSR
공식 코드https://github.com/Purushothaman-natarajan/Under-water-sonar-image-classification
공식 코드https://github.com/Pzoom522/HistSumm
공식 코드https://github.com/QData/TransferStringKernel
공식 코드Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
https://github.com/QHwan/PretrainDPI
공식 코드https://github.com/QIU023/SATS_Continual_Semantic_Seg
공식 코드SATS: Self-Attention Transfer for Continual Semantic Segmentation
https://github.com/QinbinLi/FedKT
공식 코드Practical One-Shot Federated Learning for Cross-Silo Setting
https://github.com/QingbeiGuo/SG-CNN
공식 코드https://github.com/Qrange-group/CEM
공식 코드https://github.com/QuIIL/TQx
공식 코드Towards a text-based quantitative and explainable histopathology image analysis
https://github.com/RUCAIBox/Few-Shot-KG2Text
공식 코드Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.