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 6651–6675 of 10307 papers

TitleStatusHype
Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks—0
JointMotion: Joint Self-Supervision for Joint Motion Prediction—0
An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure—0
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
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling—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
Separating and denoising seismic signals with dual-path recurrent neural network architecture—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
Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding—0
Joint Unsupervised and Supervised Training for Multilingual ASR—0
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment—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
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population—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
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition—0
KANsformer for Scalable Beamforming—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