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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5126–5150 of 10307 papers

TitleStatusHype
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
CLIP also Understands Text: Prompting CLIP for Phrase Understanding—0
Combining datasets to increase the number of samples and improve model fittingCode0
The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning—0
Fast Hierarchical Learning for Few-Shot Object Detection—0
A Survey on Heterogeneous Federated Learning—0
The effect of variable labels on deep learning models trained to predict breast density—0
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model EmulationCode0
SpaceQA: Answering Questions about the Design of Space Missions and Space Craft ConceptsCode0
Evaluating the Performance of StyleGAN2-ADA on Medical Images—0
Explainable AI based Glaucoma Detection using Transfer Learning and LIME—0
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization—0
Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters—0
Gastrointestinal Disorder Detection with a Transformer Based Approach—0
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data—0
Biological neurons act as generalization filters in reservoir computing—0
Data-driven Approaches to Surrogate Machine Learning Model Development—0
Matching Text and Audio Embeddings: Exploring Transfer-learning Strategies for Language-based Audio Retrieval—0
On Neural Consolidation for Transfer in Reinforcement Learning—0
Star-Graph Multimodal Matching Component Analysis for Data Fusion and Transfer Learning—0
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning—0
ImpressLearn: Continual Learning via Combined Task Impressions—0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts—0
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search—0
Under the Cover Infant Pose Estimation using Multimodal DataCode0
Show:102550
← PrevPage 206 of 413Next →
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