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 1251–1275 of 10307 papers

TitleStatusHype
Abstractive Summarization of Spoken and Written Instructions with BERTCode1
An Improved Person Re-identification Method by light-weight convolutional neural networkCode1
Laughter Synthesis: Combining Seq2seq modeling with Transfer LearningCode1
Knowledge Transfer via Dense Cross-Layer Mutual-DistillationCode1
Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion RecognitionCode1
Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style ConversionCode1
Unsupervised Feature Learning by Cross-Level Instance-Group DiscriminationCode1
Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer LearningCode1
aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer LearningCode1
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learningCode1
MultiCheXNet: A Multi-Task Learning Deep Network For Pneumonia-like Diseases Diagnosis From X-ray ScansCode1
Shape Adaptor: A Learnable Resizing ModuleCode1
Deep Transferring QuantizationCode1
n-Reference Transfer Learning for Saliency PredictionCode1
Principal Feature Visualisation in Convolutional Neural NetworksCode1
Bilevel Continual LearningCode1
Group Knowledge Transfer: Federated Learning of Large CNNs at the EdgeCode1
Solving Linear Inverse Problems Using the Prior Implicit in a DenoiserCode1
Pan-Cancer Computational Histopathology (PC-CHiP) analysis using deep learningCode1
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 DiagnosisCode1
Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural NetworksCode1
Rethinking CNN Models for Audio ClassificationCode1
PointContrast: Unsupervised Pre-training for 3D Point Cloud UnderstandingCode1
Generative Hierarchical Features from Synthesizing ImagesCode1
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture SearchCode1
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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