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 7251–7275 of 10307 papers

TitleStatusHype
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks—0
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19—0
Direct multimodal few-shot learning of speech and imagesCode0
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer LearningCode0
Large-Scale Generative Data-Free Distillation—0
On Knowledge Distillation for Direct Speech Translation—0
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU—0
Transfer Learning for Efficient Iterative Safety Validation—0
Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval—0
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNNCode0
Data InStance Prior (DISP) in Generative Adversarial Networks—0
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models—0
Reprogramming Language Models for Molecular Representation Learning—0
Adaptive Deep Learning for Entity Resolution by Risk Analysis—0
Detecting Insincere Questions from Text: A Transfer Learning ApproachCode0
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning—0
Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information—0
Data-Efficient Methods for Dialogue Systems—0
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks—0
Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning—0
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals—0
Boosting offline handwritten text recognition in historical documents with few labeled lines—0
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment DesignCode0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot 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