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 3026–3050 of 10307 papers

TitleStatusHype
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry—0
Who Writes the Review, Human or AI?—0
Federated and Transfer Learning for Cancer Detection Based on Image Analysis—0
On the Condition Monitoring of Bolted Joints through Acoustic Emission and Deep Transfer Learning: Generalization, Ordinal Loss and Super-Convergence—0
Domain adaptation in small-scale and heterogeneous biological datasets—0
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter—0
A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 PandemicCode0
MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction—0
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
Recent Advances of Foundation Language Models-based Continual Learning: A Survey—0
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation—0
Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records—0
An adaptive transfer learning perspective on classification in non-stationary environments—0
Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks—0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation—0
A Survey of Latent Factor Models in Recommender Systems—0
Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer—0
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift—0
Enhancing Accuracy in Generative Models via Knowledge Transfer—0
Dual-State Personalized Knowledge Tracing with Emotional Incorporation—0
Transfer Learning for Diffusion Models—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Image-Text-Image Knowledge Transferring for Lifelong Person Re-Identification with Hybrid Clothing States—0
Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification—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