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 1301–1325 of 10307 papers

TitleStatusHype
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
Generalisation Guarantees for Continual Learning with Orthogonal Gradient DescentCode1
Neural Topic Modeling with Continual Lifelong LearningCode1
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19Code1
Self-Supervised Prototypical Transfer Learning for Few-Shot ClassificationCode1
Deep Learning Enabled Semantic Communication SystemsCode1
Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax OptimalityCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
Improving accuracy and speeding up Document Image Classification through parallel systemsCode1
Adversarial Self-Supervised Contrastive LearningCode1
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesCode1
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
Knowledge Distillation Meets Self-SupervisionCode1
MemeSem:A Multi-modal Framework for Sentimental Analysis of Meme via Transfer LearningCode1
Few-shot Neural Architecture SearchCode1
Syn2Real Transfer Learning for Image Deraining using Gaussian ProcessesCode1
Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud DetectionCode1
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-AnalysisCode1
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationCode1
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image SegmentationCode1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Online Knowledge Distillation via Collaborative LearningCode1
Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and RetrievalCode1
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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