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 6776–6800 of 10307 papers

TitleStatusHype
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
Semi-supervised Chinese Word Segmentation for CLP2012—0
GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification—0
VFed-SSD: Towards Practical Vertical Federated Advertising—0
Semi-supervised Domain Adaptation in Graph Transfer Learning—0
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding—0
Blessing of Class Diversity in Pre-training—0
Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support—0
Semi-supervised Learning of Naive Bayes Classifier with feature constraints—0
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation—0
Semi-Supervised Lifelong Language Learning—0
Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives—0
Semi-supervised Object Detection: A Survey on Recent Research and Progress—0
Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset—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