SOTAVerified

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 5876–5900 of 10307 papers

TitleStatusHype
Geometry Based Machining Feature Retrieval with Inductive Transfer Learning—0
The Option Keyboard: Combining Skills in Reinforcement Learning—0
GeoTransfer : Generalizable Few-Shot Multi-View Reconstruction via Transfer Learning—0
Theoretical Guarantees of Transfer Learning—0
Gesture Recognition in Robotic Surgery: a Review—0
SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment—0
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices—0
Actionable Phrase Detection using NLP—0
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation—0
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition—0
A Statistical Theory of Regularization-Based Continual Learning—0
Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection—0
TECM: Transfer Learning-based Evidential C-Means Clustering—0
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
A transfer learning framework for weak-to-strong generalization—0
GLID: Pre-training a Generalist Encoder-Decoder Vision Model—0
Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
Associative embeddings for large-scale knowledge transfer with self-assessment—0
A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation—0
Global Estimation of Subsurface Eddy Kinetic Energy of Mesoscale Eddies Using a Multiple-input Residual Neural Network—0
Global Extreme Heat Forecasting Using Neural Weather Models—0
Global Flood Prediction: a Multimodal Machine Learning Approach—0
Selective Token Generation for Few-shot Language Modeling—0
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification—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