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 3251–3300 of 10307 papers

TitleStatusHype
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer LearningCode0
Self-Supervised Learning with Probabilistic Density Labeling for Rainfall Probability EstimationCode0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge TransferCode0
EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry SystemCode0
Evaluation of deep neural networks for traffic sign detection systemsCode0
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural NetworksCode0
Evaluating the Values of Sources in Transfer LearningCode0
Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary LearningCode0
Evaluation and Comparison of Deep Learning Methods for Pavement Crack Identification with Visual ImagesCode0
EVA: Exploring the Limits of Masked Visual Representation Learning at ScaleCode0
Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-NetCode0
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-RaysCode0
ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph EmbeddingCode0
Evaluating deep transfer learning for whole-brain cognitive decodingCode0
Contrastive learning of T cell receptor representationsCode0
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI ReconstructionCode0
Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learningCode0
Dynamic Bayesian Learning for Spatiotemporal Mechanistic ModelsCode0
CADE: Cosine Annealing Differential Evolution for Spiking Neural NetworkCode0
Estimated Depth Map Helps Image ClassificationCode0
Estimating Buildings' Parameters over Time Including Prior KnowledgeCode0
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object ClassificationCode0
Semi-supervised Multimodal Representation Learning through a Global WorkspaceCode0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
Dynamic Guidance Adversarial Distillation with Enhanced Teacher KnowledgeCode0
Exclusive Supermask Subnetwork Training for Continual LearningCode0
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data TasksCode0
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene SegmentationCode0
Environment Invariant Linear Least SquaresCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural NetworksCode0
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateCode0
Entity-aware Cross-lingual Claim Detection for Automated Fact-checkingCode0
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge TransferCode0
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement LearningCode0
Contrastive Bi-Projector for Unsupervised Domain AdaptionCode0
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited DataCode0
Ensemble of Task-Specific Language Models for Brain EncodingCode0
SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology predictionCode0
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter CollaborationCode0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
Early Life Cycle Software Defect Prediction. Why? How?Code0
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer LearningCode0
Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 GamesCode0
Enriched BERT Embeddings for Scholarly Publication ClassificationCode0
Ensemble Learning via Knowledge Transfer for CTR PredictionCode0
Enhancing textual textbook question answering with large language models and retrieval augmented generationCode0
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing ImagesCode0
Towards an efficient deep learning model for musical onset detectionCode0
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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