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 6651–6700 of 10307 papers

TitleStatusHype
Joint Photo Stream and Blog Post Summarization and Exploration—0
Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments—0
Joint PMD Tracking and Nonlinearity Compensation with Deep Neural Networks—0
Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios—0
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model—0
Joint Semantic Transfer Network for IoT Intrusion Detection—0
Joint Similarity Item Exploration and Overlapped User Guidance for Multi-Modal Cross-Domain Recommendation—0
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges—0
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
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
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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