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 7601–7650 of 10307 papers

TitleStatusHype
A Domain Adaptation Regularization for Denoising Autoencoders—0
ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data—0
Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models—0
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques—0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning—0
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion—0
Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery—0
Advances and Challenges in Meta-Learning: A Technical Review—0
Recent Advancements in Machine Learning For Cybercrime Prediction—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey—0
Advancing Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models—0
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
Advancing Extrapolative Predictions of Material Properties through Learning to Learn—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
Advancing Roadway Sign Detection with YOLO Models and Transfer Learning—0
Advancing Voice Cloning for Nepali: Leveraging Transfer Learning in a Low-Resource Language—0
Advantages of biologically-inspired adaptive neural activation in RNNs during learning—0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Adversarial Domain Adaptation Being Aware of Class Relationships—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
Adversarial Inductive Transfer Learning with input and output space adaptation—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People—0
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling—0
Adversarial Network Compression—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation—0
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation—0
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis—0
Adversarial Training Helps Transfer Learning via Better Representations—0
Adversarial Transfer Learning for Punctuation Restoration—0
Adversarial Transfer of Pose Estimation Regression—0
Adversarial Vulnerability of Active Transfer Learning—0
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition—0
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms—0
Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules—0
A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition—0
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network—0
A Feature Extraction based Model for Hate Speech Identification—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