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 951–1000 of 10307 papers

TitleStatusHype
IndicBART: A Pre-trained Model for Indic Natural Language GenerationCode1
Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease ClassificationCode1
Robust fine-tuning of zero-shot modelsCode1
A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive FusionCode1
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
roadscene2vec: A Tool for Extracting and Embedding Road Scene-GraphsCode1
AraT5: Text-to-Text Transformers for Arabic Language GenerationCode1
Task-Oriented Dialogue System as Natural Language GenerationCode1
Knowledge Base Completion Meets Transfer LearningCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Frozen Pretrained Transformers for Neural Sign Language TranslationCode1
How Hateful are Movies? A Study and Prediction on Movie SubtitlesCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Do Vision Transformers See Like Convolutional Neural Networks?Code1
AdapterHub Playground: Simple and Flexible Few-Shot Learning with AdaptersCode1
KITTI-CARLA: a KITTI-like dataset generated by CARLA SimulatorCode1
On the Opportunities and Risks of Foundation ModelsCode1
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic SegmentationCode1
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language ModelsCode1
Semantic Concentration for Domain AdaptationCode1
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image AnalysisCode1
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingCode1
TVT: Transferable Vision Transformer for Unsupervised Domain AdaptationCode1
Towards to Robust and Generalized Medical Image Segmentation FrameworkCode1
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