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 5201–5225 of 10307 papers

TitleStatusHype
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis—0
Re-examining Routing Networks for Multi-task Learning—0
Reference Resolution and Context Change in Multimodal Situated Dialogue for Exploring Data Visualizations—0
Refined Continuous Control of DDPG Actors via Parametrised Activation—0
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning—0
Refining Automatic Speech Recognition System for older adults—0
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval—0
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs—0
Region-based Convolution Neural Network Approach for Accurate Segmentation of Pelvic Radiograph—0
Region Semantically Aligned Network for Zero-Shot Learning—0
Regret Bounds for Lifelong Learning—0
Regularization techniques for fine-tuning in neural machine translation—0
Regularization Through Simultaneous Learning: A Case Study on Plant Classification—0
Compositional Transfer in Hierarchical Reinforcement Learning—0
Regularized Multi-output Gaussian Convolution Process with Domain Adaptation—0
Regularized Soft Actor-Critic for Behavior Transfer Learning—0
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks—0
Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning—0
Towards interpretable quantum machine learning via single-photon quantum walks—0
Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks—0
Reinforcement Learning by Guided Safe Exploration—0
Reinforcement Learning for Systematic FX Trading—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones—0
ReINTEL Challenge 2020: Exploiting Transfer Learning Models for Reliable Intelligence Identification on Vietnamese Social Network Sites—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