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 1751–1775 of 10307 papers

TitleStatusHype
Continual Learning with Dirichlet Generative-based Rehearsal—0
Continual Learning on the Edge with TensorFlow Lite—0
Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair—0
Accelerating evolutionary exploration through language model-based transfer learning—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling—0
Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model—0
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning—0
Application of DenseNet in Camera Model Identification and Post-processing Detection—0
Continual Learning of Natural Language Processing Tasks: A Survey—0
Continual Learning with Adaptive Weights (CLAW)—0
Continual Lifelong Learning with Neural Networks: A Review—0
Towards continually learning new languages—0
CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer—0
Adversarial Network Compression—0
Claim Detection in Biomedical Twitter Posts—0
Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Drug Formulation—0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition—0
ClaRet -- A CNN Architecture for Optical Coherence Tomography—0
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People—0
Class-based Subset Selection for Transfer Learning under Extreme Label Shift—0
Class Conditional Alignment for Partial Domain Adaptation—0
Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays—0
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network—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