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 6001–6025 of 10307 papers

TitleStatusHype
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization—0
Habitat classification from satellite observations with sparse annotations—0
Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition—0
Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection—0
A Segmentation Foundation Model for Diverse-type Tumors—0
HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images—0
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents—0
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation—0
Hand Gesture Recognition with Two Stage Approach Using Transfer Learning and Deep Ensemble Learning—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Hand Pose Classification Based on Neural Networks—0
HandsOff: Labeled Dataset Generation With No Additional Human Annotations—0
A Seed-Augment-Train Framework for Universal Digit Classification—0
Hard instance learning for quantum adiabatic prime factorization—0
Cross-Modal Distillation for RGB-Depth Person Re-Identification—0
Template Adaptation for Face Verification and Identification—0
Harmless Transfer Learning for Item Embeddings—0
Harmonizing knowledge Transfer in Neural Network with Unified Distillation—0
Harnessing Machine Learning for Discerning AI-Generated Synthetic Images—0
Self-Supervised Facial Representation Learning with Facial Region Awareness—0
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling—0
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift—0
Harnessing Transfer Learning from Swahili: Advancing Solutions for Comorian Dialects—0
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection—0
Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning—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