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 6426–6450 of 10307 papers

TitleStatusHype
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
RelO: An Overlapping Relation Extraction Dataset and ModelCode0
Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting—0
Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text ClassificationCode0
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer—0
Enhancing Clinical Information Extraction with Transferred Contextual Embeddings—0
DeFungi: Direct Mycological Examination of Microscopic Fungi Images—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning—0
Region Invariant Normalizing Flows for Mobility TransferCode0
Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios—0
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation—0
Traffic Event Detection as a Slot Filling Problem—0
Levenshtein Training for Word-level Quality EstimationCode0
Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition—0
Investigating Numeracy Learning Ability of a Text-to-Text Transfer ModelCode0
Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets—0
DeepEMO: Deep Learning for Speech Emotion RecognitionCode0
Leveraging Local Domains for Image-to-Image Translation—0
Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal—0
Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of MarathiCode0
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models—0
A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data—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