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 3751–3775 of 10307 papers

TitleStatusHype
C-Procgen: Empowering Procgen with Controllable Contexts—0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension—0
Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models—0
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models—0
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree TransformationCode0
Comparing Male Nyala and Male Kudu Classification using Transfer Learning with ResNet-50 and VGG-16—0
Transfer Learning for Structured Pruning under Limited Task Data—0
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
Enhancing Instance-Level Image Classification with Set-Level Labels—0
CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture—0
Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures—0
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency—0
Generalization in medical AI: a perspective on developing scalable models—0
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related LanguagesCode0
On Characterizing the Evolution of Embedding Space of Neural Networks using Algebraic TopologyCode0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Language Representation Projection: Can We Transfer Factual Knowledge across Languages in Multilingual Language Models?—0
Elastic Information Bottleneck—0
Sparse Contrastive Learning of Sentence Embeddings—0
Supervised domain adaptation for building extraction from off-nadir aerial images—0
Risk of Transfer Learning and its Applications in Finance—0
Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer LearningCode0
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review—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