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 2001–2025 of 10307 papers

TitleStatusHype
Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models—0
Detecting Cadastral Boundary from Satellite Images Using U-Net model—0
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection—0
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors—0
Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning—0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models—0
Ten Challenging Problems in Federated Foundation Models—0
Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces—0
ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data—0
Russo-Ukrainian war disinformation detection in suspicious Telegram channels—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
A Survey of Reinforcement Learning for Optimization in Automation—0
CSMAE~:~Cataract Surgical Masked Autoencoder (MAE) based Pre-training—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
Multifidelity Simulation-based Inference for Computationally Expensive Simulators—0
Knowledge-Guided Wasserstein Distributionally Robust Optimization—0
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations—0
Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation ModelsCode0
Optimizing Knowledge Distillation in Transformers: Enabling Multi-Head Attention without Alignment Barriers—0
Robust Indoor Localization in Dynamic Environments: A Multi-source Unsupervised Domain Adaptation Framework—0
Multi-Agent Collaboration for Multilingual Code Instruction Tuning—0
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks—0
Generative Distribution Prediction: A Unified Approach to Multimodal Learning—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