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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6826–6850 of 10307 papers

TitleStatusHype
Could you give me a hint? Generating inference graphs for defeasible reasoningCode0
Building a Question and Answer System for News Domain—0
Encoding Explanatory Knowledge for Zero-shot Science Question Answering—0
Improving Code Autocompletion with Transfer Learning—0
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research—0
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty—0
Towards Using Diachronic Distributed Word Representations as Models of Lexical Development—0
Unsupervised domain adaptation via double classifiers based on high confidence pseudo label—0
Differentially Private Transferrable Deep Learning with Membership-Mappings—0
Slash or burn: Power line and vegetation classification for wildfire prevention—0
Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition—0
Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting—0
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-TuningCode0
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction—0
GANTL: Towards Practical and Real-Time Topology Optimization with Conditional GANs and Transfer LearningCode0
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation—0
PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems—0
Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images—0
Few-Shot Learning for Image Classification of Common FloraCode0
Security Vulnerability Detection Using Deep Learning Natural Language Processing—0
Adapting Monolingual Models: Data can be Scarce when Language Similarity is HighCode0
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings—0
Continual Learning on the Edge with TensorFlow Lite—0
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation—0
Motion-Augmented Self-Training for Video Recognition at Smaller Scale—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