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 8926–8950 of 10307 papers

TitleStatusHype
Large-scale weakly-supervised pre-training for video action recognitionCode0
Stacking for Transfer Learning—0
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning—0
PCNN: Environment Adaptive Model Without Finetuning—0
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
An Information-Theoretic Metric of Transferability for Task Transfer LearningCode0
In Your Pace: Learning the Right Example at the Right Time—0
FEED: Feature-level Ensemble Effect for knowledge Distillation—0
Cross-Task Knowledge Transfer for Visually-Grounded Navigation—0
BIGSAGE: unsupervised inductive representation learning of graph via bi-attended sampling and global-biased aggregating—0
Transferring SLU Models in Novel Domains—0
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration—0
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis—0
Deep Transfer Learning for Few-Shot SAR Image Classification—0
Coevo: a collaborative design platform with artificial agents—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
DAC: The Double Actor-Critic Architecture for Learning OptionsCode0
Solo or Ensemble? Choosing a CNN Architecture for Melanoma ClassificationCode0
SEALion: a Framework for Neural Network Inference on Encrypted Data—0
Copy mechanism and tailored training for character-based data-to-text generationCode0
Attention-based Transfer Learning for Brain-computer Interface—0
Scene Graph Prediction with Limited LabelsCode0
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning—0
Lung Nodule Classification using Deep Local-Global NetworksCode0
SocialIQA: Commonsense Reasoning about Social InteractionsCode0
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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