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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/western-oc2-lab/intrusion-detection-system-using-cnn-and-transfer-learning
공식 코드A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles
https://github.com/western-oc2-lab/tinyml_evci
공식 코드On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
https://github.com/westlake-repl/adapter4rec
공식 코드https://github.com/westlake-repl/ninerec
공식 코드NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
https://github.com/wfwf10/feature-based-federated-transfer-learning
공식 코드Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning
https://github.com/wgcban/metric-cd
공식 코드Deep Metric Learning for Unsupervised Remote Sensing Change Detection
https://github.com/wglab/mint-llm
공식 코드https://github.com/whher/nmt-de-bar
공식 코드Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study
https://github.com/whu-usi3dv/whu-synthetic
공식 코드WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research
https://github.com/whu-zqh/panda
공식 코드PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation
https://github.com/whu-zqh/uika
공식 코드Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis
https://github.com/wickstrom/mixupcontrastivelearning
공식 코드Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
https://github.com/wietsedv/bertje
공식 코드https://github.com/wietsedv/bertje/tree/master/probing
공식 코드https://github.com/wietsedv/low-resource-adapt
공식 코드Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
https://github.com/wilburOne/ZeroShotEvent
공식 코드https://github.com/williamgilpin/dysts
공식 코드Chaos as an interpretable benchmark for forecasting and data-driven modelling
https://github.com/williamyang1991/DualStyleGAN
공식 코드Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
https://github.com/willtop/transfer_learning_with_reconstruction_loss
공식 코드https://github.com/willyfh/msvd-indonesian
공식 코드MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian
https://github.com/winycg/l-mcl
공식 코드Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
https://github.com/winycg/mcl
공식 코드Mutual Contrastive Learning for Visual Representation Learning
https://github.com/winycg/self-kd-lib
공식 코드MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
https://github.com/wise-east/checkdst
공식 코드Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics
https://github.com/wjmaddox/swa_gaussian
공식 코드https://github.com/wlin-at/cycda
공식 코드CycDA: Unsupervised Cycle Domain Adaptation from Image to Video
https://github.com/wmcnally/deep-darts
공식 코드DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera
https://github.com/wmyw96/EILLS
공식 코드https://github.com/wmyw96/fair
공식 코드Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
https://github.com/wngh1187/ipet
공식 코드Integrated Parameter-Efficient Tuning for General-Purpose Audio Models
https://github.com/wolfgangfuhl/publicationstuff
공식 코드Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets
https://github.com/wonchulSon/DGKD
공식 코드Densely Guided Knowledge Distillation using Multiple Teacher Assistants
https://github.com/woodssss/tl-pi-deeponet
공식 코드Transfer Learning Enhanced DeepONet for Long-Time Prediction of Evolution Equations
https://github.com/wooseong97/sel-update-mtl
공식 코드https://github.com/wq13552463699/rrc_pos_ori
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.