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 5501–5525 of 10307 papers

TitleStatusHype
Self-Transfer Learning for Fully Weakly Supervised Object Localization—0
Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI—0
Semantically Proportional Patchmix for Few-Shot Learning—0
Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training—0
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation—0
Semantic decoupled representation learning for remote sensing image change detection—0
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition—0
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector—0
Semantic Graph for Zero-Shot Learning—0
Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition—0
Semantic Parsing in Limited Resource Conditions—0
Semantic Pose using Deep Networks Trained on Synthetic RGB-D—0
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods—0
Semantic Preserving Generative Adversarial Models—0
Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation—0
Semantics Distortion and Style Matter: Towards Source-free UDA for Panoramic Segmentation—0
Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images—0
Semantic Segmentation of Skin Lesions using a Small Data Set—0
Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning—0
Semantic Segmentation Using Transfer Learning on Fisheye Images—0
sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model—0
SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection—0
Semi Few-Shot Attribute Translation—0
Semisupervised Adversarial Neural Networks for Cyber Security Transfer Learning—0
Semi-supervised and Transfer learning approaches for low resource sentiment classification—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