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 8001–8050 of 10307 papers

TitleStatusHype
Vocabulary Transfer for Biomedical Texts: Add Tokens if You Can Not Add Data—0
Voice Aging with Audio-Visual Style Transfer—0
Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning—0
Volatility Forecasting with 1-dimensional CNNs via transfer learning—0
Voting-based Approaches For Differentially Private Federated Learning—0
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications—0
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication—0
Wasserstein Contrastive Representation Distillation—0
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis—0
Wasserstein Selective Transfer Learning for Cross-domain Text Mining—0
Wasserstein Transfer Learning—0
Water quality polluted by total suspended solids classified within an Artificial Neural Network approach—0
Wavelet-based Autoencoder and EfficientNet for Schizophrenia Detection from EEG Signals—0
WAVE: Weight Template for Adaptive Initialization of Variable-sized Models—0
Weakly Semi-Supervised Detection in Lung Ultrasound Videos—0
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation—0
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging—0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification—0
Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset—0
Weakly-Supervised Localization and Classification of Proximal Femur Fractures—0
Weakly Supervised Monocular 3D Detection with a Single-View Image—0
Weakly Supervised One-Shot Detection with Attention Similarity Networks—0
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration—0
Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection—0
Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound—0
Weak-Shot Object Detection Through Mutual Knowledge Transfer—0
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning—0
Web image search engine based on LSH index and CNN Resnet50—0
Webly Supervised Learning for Skin Lesion Classification—0
Web-Scale Training for Face Identification—0
Web Table Classification based on Visual Features—0
WebVision Challenge: Visual Learning and Understanding With Web Data—0
WeChat Neural Machine Translation Systems for WMT20—0
WeChat Neural Machine Translation Systems for WMT21—0
Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling—0
Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling—0
Weighted Multisource Tradaboost—0
Weighted Sampling for Masked Language Modeling—0
Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Weight Squeezing: Reparameterization for Compression and Fast Inference—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Weight subcloning: direct initialization of transformers using larger pretrained ones—0
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers—0
What can we learn about CNNs from a large scale controlled object dataset?—0
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text—0
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications—0
What Do We Maximize in Self-Supervised Learning?—0
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
What Makes for Hierarchical Vision Transformer?—0
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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