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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6926–6950 of 10307 papers

TitleStatusHype
Whit’s the Richt Pairt o Speech: PoS tagging for Scots—0
Low-Resource Neural Machine Translation for Southern African Languages—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
Offensive language identification in Dravidian code mixed social media text—0
Cross-Lingual Transfer Learning for Hate Speech Detection—0
LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts—0
MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations—0
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian LanguagesCode0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets—0
EDIOne@LT-EDI-EACL2021: Pre-trained Transformers with Convolutional Neural Networks for Hope Speech Detection.—0
GCDH@LT-EDI-EACL2021: XLM-RoBERTa for Hope Speech Detection in English, Malayalam, and Tamil—0
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models—0
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time—0
Drowsiness Detection Based On Driver Temporal Behavior Using a New Developed Dataset—0
LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method—0
DA-DETR: Domain Adaptive Detection Transformer with Information Fusion—0
Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine TranslationCode0
Transfer Learning for Node Regression Applied to Spreading PredictionCode0
A resource-efficient method for repeated HPO and NAS problems—0
Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident LearningCode0
Source-Free Domain Adaptation for Semantic Segmentation—0
Benchmarking Representation Learning for Natural World Image CollectionsCode0
Robust Audio-Visual Instance Discrimination—0
Zero-shot Adversarial Quantization—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