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 9351–9375 of 10307 papers

TitleStatusHype
Language Identification with Deep Bottleneck Features—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation—0
Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition—0
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks—0
Inspiration Learning through Preferences—0
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion RecognitionCode0
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics—0
LSTM knowledge transfer for HRV-based sleep staging—0
Combined Reinforcement Learning via Abstract RepresentationsCode0
Ensemble of Convolutional Neural Networks for Automatic Grading of Diabetic Retinopathy and Macular Edema—0
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation—0
Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning—0
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real—0
Instance-based Deep Transfer Learning—0
A Deeper Look at 3D Shape Classifiers—0
Driving Experience Transfer Method for End-to-End Control of Self-Driving Cars—0
Five lessons from building a deep neural network recommender—0
Parameter Transfer Extreme Learning Machine based on Projective ModelCode0
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion ClassificationCode0
Trivial Transfer Learning for Low-Resource Neural Machine Translation—0
Zero-shot User Intent Detection via Capsule Neural NetworksCode0
On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains—0
A New Large Scale Dynamic Texture Dataset with Application to ConvNet Understanding—0
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation—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