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 4226–4250 of 10307 papers

TitleStatusHype
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer—0
Enhanced Transport Distance for Unsupervised Domain Adaptation—0
Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair—0
Adversarial Network Compression—0
Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion—0
Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation—0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning—0
Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning—0
Enhanced dynamic sign language recognition using slowfast networks—0
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment—0
Application of DenseNet in Camera Model Identification and Post-processing Detection—0
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Accelerating evolutionary exploration through language model-based transfer learning—0
Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model—0
Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning—0
English-Basque Statistical and Neural Machine Translation—0
Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks—0
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods—0
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition—0
Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Drug Formulation—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