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 6626–6650 of 10307 papers

TitleStatusHype
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
SB_NITK at MEDIQA 2021: Leveraging Transfer Learning for Question Summarization in Medical Domain—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge—0
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media—0
Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis—0
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings—0
Scalable Differential Privacy With Sparse Network Finetuning—0
Scalable Forward-Forward Algorithm—0
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency—0
Scalable Greedy Algorithms for Transfer Learning—0
Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets—0
Scalable Hyperparameter Transfer Learning—0
Scalable Learning of Segment-Level Traffic Congestion Functions—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Bibliometric-enhanced Information Retrieval: 2nd International BIR Workshop—0
Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Models—0
Bidirectional Language Models Are Also Few-shot Learners—0
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization—0
Scalable Multi-Task Transfer Learning for Molecular Property Prediction—0
Scalable Neural Data Server: A Data Recommender for Transfer Learning—0
Scalable Transfer Learning with Expert Models—0
Scalable Weight Reparametrization for Efficient Transfer Learning—0
Scalarization for Multi-Task and Multi-Domain Learning at Scale—0
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation—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