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 6951–6975 of 10307 papers

TitleStatusHype
Learning Deep Representations via Contrastive Learning for Instance Retrieval—0
Learning Deep Representations with Probabilistic Knowledge Transfer—0
An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task—0
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees—0
Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition—0
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese—0
Learning Evolution via Optimization Knowledge Adaptation—0
Learning Execution through Neural Code Fusion—0
Siloed Federated Learning for Multi-Centric Histopathology Datasets—0
Sim2real transfer learning for 3D human pose estimation: motion to the rescue—0
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly—0
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning—0
Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks—0
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification—0
Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining—0
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning—0
Learning from Auxiliary Sources in Argumentative Revision Classification—0
Learning from Explanations and Demonstrations: A Pilot Study—0
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning—0
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets—0
An introduction to domain adaptation and transfer learning—0
Learning from Higher-Layer Feature Visualizations—0
Learning from LDA using Deep Neural Networks—0
Self-Supervised Real-to-Sim Scene Generation—0
Learning from Synthetic Data for Visual Grounding—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