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 5376–5400 of 10307 papers

TitleStatusHype
A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects—0
A Transfer Learning Based Model for Text Readability Assessment in German—0
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks—0
On the Generalization for Transfer Learning: An Information-Theoretic Analysis—0
RE-Tagger: A light-weight Real-Estate Image Classifier—0
Transferability-Guided Cross-Domain Cross-Task Transfer Learning—0
An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure—0
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion RecognitionCode0
Real-Time And Robust 3D Object Detection with Roadside LiDARs—0
Dual-Correction Adaptation Network for Noisy Knowledge Transfer—0
Beyond Transfer Learning: Co-finetuning for Action Localisation—0
On Improving the Performance of Glitch Classification for Gravitational Wave Detection by using Generative Adversarial Networks—0
The Power of Transfer Learning in Agricultural Applications: AgriNet—0
G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning—0
Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning—0
Deep learning based Hand gesture recognition system and design of a Human-Machine Interface—0
An Embedding-Dynamic Approach to Self-supervised Learning—0
Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database—0
Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation—0
Low-resource Low-footprint Wake-word Detection using Knowledge Distillation—0
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms—0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
A Unified Meta-Learning Framework for Dynamic Transfer LearningCode0
Vision-and-Language PretrainingCode0
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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