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 10151–10175 of 10307 papers

TitleStatusHype
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive LearningCode0
Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled DataCode0
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human ActivityCode0
Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled DataCode0
Revisiting Model's Uncertainty and Confidences for Adversarial Example DetectionCode0
Selective Pre-training for Private Fine-tuningCode0
Self-Distillation with Meta Learning for Knowledge Graph CompletionCode0
Self-Driving Car Steering Angle Prediction Based on Image RecognitionCode0
Self-Evolved Dynamic Expansion Model for Task-Free Continual LearningCode0
Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning StudyCode0
Self-supervised Knowledge Distillation Using Singular Value DecompositionCode0
Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging dataCode0
Self-supervised learning for skin cancer diagnosis with limited training dataCode0
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentationCode0
Self-Supervised Learning with Probabilistic Density Labeling for Rainfall Probability EstimationCode0
Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy imagesCode0
Self-supervised Pre-training of Text RecognizersCode0
Self-supervised Transfer Learning for Instance Segmentation through Physical InteractionCode0
Self-training solutions for the ICCV 2023 GeoNet ChallengeCode0
Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural NetworksCode0
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain RecommendationCode0
Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-TuningCode0
Semi-Online Knowledge DistillationCode0
Semi-supervised Transfer Learning for Image Rain RemovalCode0
Semi-supervised Knowledge Transfer for Deep Learning from Private Training DataCode0
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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