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 4976–5000 of 10307 papers

TitleStatusHype
Joint Unsupervised and Supervised Training for Multilingual ASR—0
Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity—0
Jumpstarting Surgical Computer Vision—0
jurBERT: A Romanian BERT Model for Legal Judgement Prediction—0
JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language Models—0
Just rotate it! Uncertainty estimation in closed-source models via multiple queries—0
JutePestDetect: An Intelligent Approach for Jute Pest Identification Using Fine-Tuned Transfer Learning—0
KANsformer for Scalable Beamforming—0
Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition—0
KATO: Knowledge Alignment and Transfer for Transistor Sizing of Different Design and Technology—0
KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT—0
Keratoconus Classifier for Smartphone-based Corneal Topographer—0
Kernel Alignment for Unsupervised Transfer Learning—0
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
Accelerated Bayesian Optimization throughWeight-Prior Tuning—0
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks—0
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning—0
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning—0
A Two-Step Deep Learning Method for 3DCT-2DUS Kidney Registration During Breathing—0
KITE: A Kernel-based Improved Transferability Estimation Method—0
KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus—0
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification—0
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node 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