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 8726–8750 of 10307 papers

TitleStatusHype
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
Self-Attentional Credit Assignment for Transfer in Reinforcement LearningCode0
A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images—0
Transfer Learning Across Simulated Robots With Different Sensors—0
Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches—0
Gated Recurrent Neural Network Approach for Multilabel Emotion Detection in Microblogs—0
The iWildCam 2019 Challenge Dataset—0
Cataloging Accreted Stars within Gaia DR2 using Deep Learning—0
Low-supervision urgency detection and transfer in short crisis messages—0
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis—0
Cross-Lingual Transfer Learning for Question Answering—0
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning—0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera—0
Aesthetic Attributes Assessment of ImagesCode0
Predicting engagement in online social networks: Challenges and opportunities—0
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges—0
LakhNES: Improving multi-instrumental music generation with cross-domain pre-trainingCode0
Multilingual Universal Sentence Encoder for Semantic Retrieval—0
Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier—0
Transfer Learning from Audio-Visual Grounding to Speech Recognition—0
Blending-target Domain Adaptation by Adversarial Meta-Adaptation NetworksCode0
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data—0
Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet—0
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings—0
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine TranslationCode0
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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