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 5026–5050 of 10307 papers

TitleStatusHype
Transformers on Multilingual Clause-Level MorphologyCode0
Could Giant Pretrained Image Models Extract Universal Representations?—0
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels—0
DEArt: Dataset of European Art—0
MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid—0
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer—0
A Study on Using Different Audio Lengths in Transfer Learning for Improving Chainsaw Sound Recognition—0
An Empirical Study on Data Leakage and Generalizability of Link Prediction Models for Issues and Commits—0
Mandarin-English Code-Switching Speech Recognition System for Specific Domain—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Taiwanese-Accented Mandarin and English Multi-Speaker Talking-Face Synthesis System—0
Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights—0
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched FlowsCode0
Transfer Learning with Kernel Methods—0
Teacher-student curriculum learning for reinforcement learning—0
Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5Code0
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets—0
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples—0
Improving Cause-of-Death Classification from Verbal Autopsy Reports—0
Actionable Phrase Detection using NLP—0
Transfer Learning with Synthetic Corpora for Spatial Role Labeling and ReasoningCode0
Subsidiary Prototype Alignment for Universal Domain Adaptation—0
A Survey on Causal Representation Learning and Future Work for Medical Image AnalysisCode0
Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters—0
Space-Time Graph Neural Networks with Stochastic Graph Perturbations—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