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 1076–1100 of 10307 papers

TitleStatusHype
Affordance Transfer Learning for Human-Object Interaction DetectionCode1
Efficient transfer learning for NLP with ELECTRACode1
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance ComputingCode1
Few-Shot Keyword Spotting in Any LanguageCode1
A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained ClassificationCode1
AmbiFC: Fact-Checking Ambiguous Claims with EvidenceCode1
MultiReQA: A Cross-Domain Evaluation forRetrieval Question Answering ModelsCode1
Many-to-English Machine Translation Tools, Data, and Pretrained ModelsCode1
Going deeper with Image TransformersCode1
Deep Image Harmonization by Bridging the Reality GapCode1
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised ClassificationCode1
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing DataCode1
Dynamic Domain Adaptation for Efficient InferenceCode1
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning ModelsCode1
Sparse Object-level Supervision for Instance Segmentation with Pixel EmbeddingsCode1
3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer LearningCode1
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic VehiclesCode1
OTCE: A Transferability Metric for Cross-Domain Cross-Task RepresentationsCode1
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask BenchmarkCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
Scaling Local Self-Attention for Parameter Efficient Visual BackbonesCode1
MasakhaNER: Named Entity Recognition for African LanguagesCode1
Intra-Inter Camera Similarity for Unsupervised Person Re-IdentificationCode1
Efficient Visual Pretraining with Contrastive DetectionCode1
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using SmartphonesCode1
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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