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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 6176–6200 of 10307 papers

TitleStatusHype
Parameter-Efficient Abstractive Question Answering over Tables and over Text—0
Cross-domain Named Entity Recognition via Graph Matching—0
Sharper Reasons: Argument Mining Leveraged with Confluent Knowledge—0
All Birds with One Stone: Multi-task Learning for Inference with One Forward Pass—0
Composable Sparse Fine-Tuning for Cross-Lingual Transfer—0
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning—0
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Zero-Shot Low-Resource Language Temporal Expression Extraction—0
Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy ImagesCode0
Two Front-Ends, One Model : Fusing Heterogeneous Speech Features for Low Resource ASR with Multilingual Pre-Training—0
Transfer Learning Capabilities of Untrained Neural Networks for MIMO CSI Recreation—0
Joint Unsupervised and Supervised Training for Multilingual ASR—0
Is One Teacher Model Enough to Transfer Knowledge to a Student Model?Code0
Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning—0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS—0
Improving Compound Activity Classification via Deep Transfer and Representation LearningCode0
Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection ModelsCode0
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Entropy optimized semi-supervised decomposed vector-quantized variational autoencoder model based on transfer learning for multiclass text classification and generation—0
Prune Once for All: Sparse Pre-Trained Language Models—0
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks—0
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering—0
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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