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 976–1000 of 10307 papers

TitleStatusHype
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code EmbeddingCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
SMOTified-GAN for class imbalanced pattern classification problemsCode1
EENLP: Cross-lingual Eastern European NLP IndexCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
Transfer Learning for Pose Estimation of Illustrated CharactersCode1
Boosting Weakly Supervised Object Detection via Learning Bounding Box AdjustersCode1
Self-supervised Audiovisual Representation Learning for Remote Sensing DataCode1
Pre-trained Models for Sonar ImagesCode1
Unsupervised Cross-Modal Distillation for Thermal Infrared TrackingCode1
Transferable Dialogue Systems and User SimulatorsCode1
Target-Oriented Fine-tuning for Zero-Resource Named Entity RecognitionCode1
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingCode1
Improving Mask R-CNN for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological ImagesCode1
Know Thyself: Transferable Visual Control Policies Through Robot-AwarenessCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
Non-binary deep transfer learning for image classificationCode1
AgileGAN: stylizing portraits by inversion-consistent transfer learningCode1
TFix: Learning to Fix Coding Errors with a Text-to-Text TransformerCode1
Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelismCode1
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic SegmentationCode1
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge TransferCode1
Learning Efficient Vision Transformers via Fine-Grained Manifold DistillationCode1
Memory Efficient Meta-Learning with Large ImagesCode1
Shared Data and Algorithms for Deep Learning in Fundamental PhysicsCode1
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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