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 5851–5875 of 10307 papers

TitleStatusHype
Multi-task Representation Learning with Stochastic Linear Bandits—0
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence RoboticsCode0
BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search enginesCode0
Cross-Task Knowledge Distillation in Multi-Task Recommendation—0
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer—0
VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion—0
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations—0
From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French—0
Domain Adaptation of low-resource Target-Domain models using well-trained ASR Conformer Models—0
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy BreachesCode0
Semantically Proportional Patchmix for Few-Shot Learning—0
Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations—0
CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentationCode0
Scalable approach to many-body localization via quantum dataCode0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning—0
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable SensorsCode0
Knowledge Distillation with Deep SupervisionCode0
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers—0
Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models—0
An Intrusion Response System utilizing Deep Q-Networks and System PartitionsCode0
NeuPL: Neural Population Learning—0
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes—0
DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion—0
COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasetsCode0
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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