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 10101–10150 of 10307 papers

TitleStatusHype
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery—0
Fine-Grained Temporal Relation Extraction—0
Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks—0
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning—0
FineText: Text Classification via Attention-based Language Model Fine-tuning—0
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification—0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model—0
Fine-tuning -- a Transfer Learning approach—0
Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment—0
Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks—0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally—0
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing—0
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders—0
Fine-Tuning Models Comparisons on Garbage Classification for Recyclability—0
Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation—0
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods—0
Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning—0
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks—0
FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms—0
FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments—0
A Versatile Agent for Fast Learning from Human Instructors—0
FisHook -- An Optimized Approach to Marine Specie Classification using MobileNetV2—0
Fish-TViT: A novel fish species classification method in multi water areas based on transfer learning and vision transformer—0
Five lessons from building a deep neural network recommender—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning—0
Flexible Transfer Learning under Support and Model Shift—0
FlexPose: Pose Distribution Adaptation with Limited Guidance—0
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy—0
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis—0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis—0
Folding membrane proteins by deep transfer learning—0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model—0
Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data—0
Forensic Dental Age Estimation Using Modified Deep Learning Neural Network—0
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation—0
Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis—0
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance—0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
Forward and Backward Knowledge Transfer for Sentiment Classification—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
Foundation Model's Embedded Representations May Detect Distribution Shift—0
Foundations of Multivariate Distributional Reinforcement Learning—0
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence—0
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning—0
Framework Construction of an Adversarial Federated Transfer Learning Classifier—0
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks—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