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 1926–1950 of 10307 papers

TitleStatusHype
A Framework for Hierarchical Multilingual Machine Translation—0
Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization—0
A Robust Illumination-Invariant Camera System for Agricultural Applications—0
Combined Scaling for Zero-shot Transfer Learning—0
Combinets: Creativity via Recombination of Neural Networks—0
Combining Behaviors with the Successor Features Keyboard—0
Combining Convolution and Recursive Neural Networks for Sentiment Analysis—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Action Recognition using Transfer Learning and Majority Voting for CSGO—0
Combining Federated Learning and Control: A Survey—0
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing—0
Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification—0
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection—0
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information—0
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
Come hither or go away? Recognising pre-electoral coalition signals in the news—0
Command-line Risk Classification using Transformer-based Neural Architectures—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations—0
Cross-database non-frontal facial expression recognition based on transductive deep transfer learning—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