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 2051–2075 of 10307 papers

TitleStatusHype
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment—0
Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation—0
Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case StudyCode0
LLM-USO: Large Language Model-based Universal Sizing Optimizer—0
Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning StudyCode0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
Geometric Framework for Cell Oversegmentation—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Learning Hyperparameters via a Data-Emphasized Variational ObjectiveCode0
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning—0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study—0
SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks—0
Imagine with the Teacher: Complete Shape in a Multi-View Distillation Way—0
Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation using Deep Learning-based Image Processing Techniques—0
Improving Quality Control Of MRI Images Using Synthetic Motion Data—0
Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution—0
Lightspeed Geometric Dataset Distance via Sliced Optimal TransportCode0
SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR ImagingCode0
A Zero-Shot Generalization Framework for LLM-Driven Cross-Domain Sequential Recommendation—0
Transfer Learning for Nonparametric Contextual Dynamic PricingCode0
Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations—0
Predicting concentration levels of air pollutants by transfer learning and recurrent neural network—0
Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—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