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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1075 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
DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised DataCode1
Deep Learning Based Assessment of Synthetic Speech NaturalnessCode1
T2NER: Transformers based Transfer Learning Framework for Named Entity RecognitionCode1
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
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual LearningCode1
What to Pre-Train on? Efficient Intermediate Task SelectionCode1
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
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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