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 1826–1850 of 10307 papers

TitleStatusHype
2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion—0
Federated Transfer Component Analysis Towards Effective VNF Profiling—0
FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural NetworksCode0
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection—0
Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition—0
Causally Abstracted Multi-armed BanditsCode0
A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation—0
Self-supervised visual learning in the low-data regime: a comparative evaluation—0
On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use CaseCode0
Asking and Answering Questions to Extract Event-Argument StructuresCode0
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacousticsCode3
Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring—0
OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned ImagesCode1
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks—0
No Train but Gain: Language Arithmetic for training-free Language Adapters enhancementCode0
Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification—0
Where to Mask: Structure-Guided Masking for Graph Masked AutoencodersCode1
M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition—0
How we Learn Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching—0
Unified Unsupervised Salient Object Detection via Knowledge TransferCode1
Automated Long Answer Grading with RiceChem DatasetCode0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning—0
Machine Learning Techniques for MRI Data Processing at Expanding Scale—0
ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face SynthesisCode1
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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