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 5226–5250 of 10307 papers

TitleStatusHype
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning—0
Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence FunctionCode0
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models—0
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy—0
Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican—0
SFS-A68: a dataset for the segmentation of space functions in apartment buildingsCode0
Learning ASR pathways: A sparse multilingual ASR model—0
Identification of Cognitive Workload during Surgical Tasks with Multimodal Deep Learning—0
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health—0
Automatically Score Tissue Images Like a Pathologist by Transfer Learning—0
Cross-Modal Knowledge Transfer Without Task-Relevant Source Data—0
Exploring Target Representations for Masked AutoencodersCode0
Improving plant disease classification by adaptive minimal ensembling—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
Blessing of Class Diversity in Pre-training—0
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19—0
Transfer Learning and Vision Transformer based State-of-Health prediction of Lithium-Ion Batteries—0
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students—0
Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification—0
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane DetectionCode0
Reference Resolution and Context Change in Multimodal Situated Dialogue for Exploring Data Visualizations—0
A Study on Representation Transfer for Few-Shot Learning—0
Federated Transfer Learning with Multimodal Data—0
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—0
Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain—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