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 8976–9000 of 10307 papers

TitleStatusHype
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients—0
Can Attention-based Transformers Explain or Interpret Cyberbullying Detection?—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach—0
Cancer Registry Information Extraction via Transfer Learning—0
Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset—0
Bridging Domain Gap for Flight-Ready Spaceborne Vision—0
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?—0
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?—0
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning—0
Can Multisensory Cues in VR Help Train Pattern Recognition to Citizen Scientists?—0
Canoe : A System for Collaborative Learning for Neural Nets—0
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories—0
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—0
Pushing the Limits of AMR Parsing with Self-Learning—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer—0
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches—0
Capsule networks for low-data transfer learning—0
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer—0
Adaptive Transfer Learning of Multi-View Time Series Classification—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