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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9051–9075 of 10307 papers

TitleStatusHype
Could you give me a hint? Generating inference graphs for defeasible reasoningCode0
Counterfactual Detection meets Transfer LearningCode0
Mixture of Online and Offline Experts for Non-stationary Time SeriesCode0
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNNCode0
COVID-19 Detection Using Transfer Learning Approach from Computed Tomography ImagesCode0
Creativity Inspired Zero-Shot LearningCode0
Self-Attentional Credit Assignment for Transfer in Reinforcement LearningCode0
Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to AnalysisCode0
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot LearningCode0
Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture FeaturesCode0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Cross-corpus Readability Compatibility Assessment for English TextsCode0
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data AugmentationCode0
Cross-Dimension Affinity Distillation for 3D EM Neuron SegmentationCode0
Cross-dimensional transfer learning in medical image segmentation with deep learningCode0
Cross-domain and Cross-dimension Learning for Image-to-Graph TransformersCode0
Cross-Domain Conditional Diffusion Models for Time Series ImputationCode0
Cross-Domain Few-Shot Graph ClassificationCode0
Cross-Domain NER using Cross-Domain Language ModelingCode0
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic ImageryCode0
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes FrameworkCode0
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and EquityCode0
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified FrameworkCode0
Cross-Lingual Argumentative Relation Identification: from English to PortugueseCode0
Cross-lingual Dependency Parsing with Unlabeled Auxiliary LanguagesCode0
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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