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 926–950 of 10307 papers

TitleStatusHype
TCube: Domain-Agnostic Neural Time-series NarrationCode1
Vector-quantized Image Modeling with Improved VQGANCode1
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command RecognitionCode1
Towards a Unified View of Parameter-Efficient Transfer LearningCode1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
Study on Transfer Learning Capabilities for Pneumonia Classification in Chest-X-Rays ImageCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP TasksCode1
PoNet: Pooling Network for Efficient Token Mixing in Long SequencesCode1
Transfer Learning U-Net Deep Learning for Lung Ultrasound SegmentationCode1
Self-Supervised Generative Style Transfer for One-Shot Medical Image SegmentationCode1
MEBeauty: a multi-ethnic facial beauty dataset in-the-wildCode1
Revisiting Self-Training for Few-Shot Learning of Language ModelCode1
Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell ManufacturingCode1
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional NetworkCode1
Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community DetectionCode1
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot LearningCode1
PASS: An ImageNet replacement for self-supervised pretraining without humansCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism DetectionCode1
Does Pretraining for Summarization Require Knowledge Transfer?Code1
Zero-Shot Dialogue State Tracking via Cross-Task TransferCode1
Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray ImagesCode1
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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