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 5001–5025 of 10307 papers

TitleStatusHype
k-Nearest Neighbor Augmented Neural Networks for Text Classification—0
Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning—0
k-NN as a Simple and Effective Estimator of Transferability—0
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge—0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Knowledge-Based Learning through Feature Generation—0
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation—0
Knowledge Distillation Label Smoothing: Fact or Fallacy?—0
Knowledge Distillation Based Semantic Communications For Multiple Users—0
Knowledge Distillation of Black-Box Large Language Models—0
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions—0
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher—0
Knowledge Distillation Under Ideal Joint Classifier Assumption—0
Knowledge Distillation via Token-level Relationship Graph—0
Knowledge distillation with error-correcting transfer learning for wind power prediction—0
Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification—0
Knowledge Efficient Deep Learning for Natural Language Processing—0
EA-KD: Entropy-based Adaptive Knowledge Distillation—0
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports—0
Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving—0
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions—0
Knowledge-Guided Wasserstein Distributionally Robust Optimization—0
Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing—0
Knowledge Inheritance for Pre-trained Language Models—0
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer—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