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 926–950 of 10307 papers

TitleStatusHype
Variable Extraction for Model Recovery in Scientific Literature—0
BERT-Based Approach for Automating Course Articulation Matrix Construction with Explainable AICode0
POS-tagging to highlight the skeletal structure of sentencesCode0
Transforming Static Images Using Generative Models for Video Salient Object Detection—0
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote SensingCode0
Revised Regularization for Efficient Continual Learning through Correlation-Based Parameter Update in Bayesian Neural Networks—0
Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning—0
Prior-based Objective Inference Mining Potential Uncertainty for Facial Expression RecognitionCode0
Domain Adaptive Unfolded Graph Neural Networks—0
MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Collaborative Learning—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—0
Multivariate and Online Transfer Learning with Uncertainty Quantification—0
MLDGG: Meta-Learning for Domain Generalization on Graphs—0
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers—0
Compression of Higher Order Ambisonics with Multichannel RVQGAN—0
Transmission Line Defect Detection Based on UAV Patrol Images and Vision-language Pretraining—0
Efficient Transfer Learning for Video-language Foundation ModelsCode0
DiHuR: Diffusion-Guided Generalizable Human Reconstruction—0
Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment—0
FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacy—0
Everything is a Video: Unifying Modalities through Next-Frame Prediction—0
Unlocking Transfer Learning for Open-World Few-Shot Recognition—0
Causal Time-Series Synchronization for Multi-Dimensional Forecasting—0
Federated Domain Generalization via Prompt Learning and AggregationCode0
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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