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 7626–7650 of 10307 papers

TitleStatusHype
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
The Relevance of the Source Language in Transfer Learning for ASR—0
A multilingual training strategy for low resource Text to Speech—0
Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework—0
A Multi-input Multi-output Transformer-based Hybrid Neural Network for Multi-class Privacy Disclosure Detection—0
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations—0
MMW-Carry: Enhancing Carry Object Detection through Millimeter-Wave Radar-Camera Fusion—0
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding—0
MOBA: Multi-teacher Model Based Reinforcement Learning—0
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality—0
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training—0
MobileTL: On-device Transfer Learning with Inverted Residual Blocks—0
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning—0
A Multi-class Approach -- Building a Visual Classifier based on Textual Descriptions using Zero-Shot Learning—0
MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models—0
Modality-bridge Transfer Learning for Medical Image Classification—0
Source Data Selection for Brain-Computer Interfaces based on Simple Features—0
ModalPrompt:Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models—0
Model Adaptation for Personalized Opinion Analysis—0
Model Adaption Object Detection System for Robot—0
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing—0
Source data selection for out-of-domain generalization—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