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Transfer Learning

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.

( Image credit: Subodh Malgonde )

Papers

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Showing 8101–8125 of 10307 papers

TitleStatusHype
XLTime: A Cross-Lingual Knowledge Transfer Framework for Zero-Shot Low-Resource Language Temporal Expression Extraction—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction—0
X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI—0
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup—0
X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data—0
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection—0
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition—0
XVO: Generalized Visual Odometry via Cross-Modal Self-Training—0
YANMTT: Yet Another Neural Machine Translation Toolkit—0
Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning—0
以遷移學習改善深度神經網路模型於中文歌詞情緒辨識 (Using Transfer Learning to Improve Deep Neural Networks for Lyrics Emotion Recognition in Chinese)—0
Yoga Pose Classification Using Transfer Learning—0
YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection—0
YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)—0
You Are What You Write: Preserving Privacy in the Era of Large Language Models—0
"You eat with your eyes first": Optimizing Yelp Image Advertising—0
ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection—0
Zero-Annotation Object Detection with Web Knowledge Transfer—0
Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation—0
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation—0
Zero Resource Cross-Lingual Part Of Speech Tagging—0
Zero-Resource Multilingual Model Transfer: Learning What to Share—0
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models—0
Zero-shot Adversarial Quantization—0
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified