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 6051–6075 of 10307 papers

TitleStatusHype
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training—0
Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios—0
Monitoring crop phenology with street-level imagery using computer vision—0
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning—0
An Empirical Study on Transfer Learning for Privilege Review—0
Chimpanzee voice prints? Insights from transfer learning experiments from human voices—0
Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data—0
One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots—0
Know Thy Strengths: Comprehensive Dialogue State Tracking DiagnosticsCode0
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection—0
A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19Code0
On the Use of External Data for Spoken Named Entity RecognitionCode0
Epigenomic language models powered by Cerebras—0
Maximum Bayes Smatch Ensemble Distillation for AMR ParsingCode0
Long-tail Recognition via Compositional Knowledge Transfer—0
Large Language Models are not Models of Natural Language: they are Corpus Models—0
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant SetupCode0
Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement—0
Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry—0
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical recordsCode0
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representationsCode0
Analysis and Prediction of NLP Models Via Task EmbeddingsCode0
KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentationCode0
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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