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 1051–1100 of 10307 papers

TitleStatusHype
Multiple Meta-model Quantifying for Medical Visual Question AnsweringCode1
Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networksCode1
Graph-Free Knowledge Distillation for Graph Neural NetworksCode1
Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID DataCode1
Wav2KWS: Transfer Learning from Speech Representations for Keyword SpottingCode1
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State TrackingCode1
FNet: Mixing Tokens with Fourier TransformsCode1
Infrared Image Super-Resolution via Transfer Learning and PSRGANCode1
Determining Chess Game State From an ImageCode1
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data DomainsCode1
Facial Emotion Recognition Using Transfer Learning in the Deep CNNCode1
Mutual Contrastive Learning for Visual Representation LearningCode1
Deep Learning Based Assessment of Synthetic Speech NaturalnessCode1
T2NER: Transformers based Transfer Learning Framework for Named Entity RecognitionCode1
DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised DataCode1
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and ColonoscopyCode1
X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question AnsweringCode1
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine TranslationCode1
Neural Transfer Learning for Repairing Security Vulnerabilities in C CodeCode1
What to Pre-Train on? Efficient Intermediate Task SelectionCode1
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual LearningCode1
XTREME-R: Towards More Challenging and Nuanced Multilingual EvaluationCode1
Fruit Quality and Defect Image Classification with Conditional GAN Data AugmentationCode1
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
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