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 5376–5400 of 10307 papers

TitleStatusHype
Long-Term Ensemble Learning of Visual Place Classifiers—0
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks—0
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling—0
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition—0
Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations—0
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples—0
LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization—0
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery—0
Love Thy Neighbor: Combining Two Neighboring Low-Resource Languages for Translation—0
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer—0
Low-Fidelity Video Encoder Optimization for Temporal Action Localization—0
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning—0
Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation—0
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation—0
Low-Resource Adaptation of Neural NLP Models—0
Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models—0
Low-resource Deep Entity Resolution with Transfer and Active Learning—0
Low-Resource End-to-end Sanskrit TTS using Tacotron2, WaveGlow and Transfer Learning—0
Low-resource Information Extraction with the European Clinical Case Corpus—0
Low-resource Low-footprint Wake-word Detection using Knowledge Distillation—0
Low-resource named entity recognition via multi-source projection: Not quite there yet?—0
Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields—0
Low-Resource Neural Machine Translation for Southern African Languages—0
Low Resource Sequence Tagging using Sentence Reconstruction—0
Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models—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