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 8226–8250 of 10307 papers

TitleStatusHype
Conditional Deep Gaussian Processes: multi-fidelity kernel learningCode0
Finding Quantum Critical Points with Neural-Network Quantum States—0
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks—0
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach—0
Extracting dispersion curves from ambient noise correlations using deep learning—0
Transfer Learning for HVAC System Fault DetectionCode0
Classification of Chest Diseases using Wavelet Transforms and Transfer Learning—0
Neural Sign Language Translation by Learning Tokenization—0
Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor—0
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs—0
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications—0
Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks—0
Siamese Content Loss Networks for Highly Imbalanced Medical Image Segmentation—0
3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks—0
Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings—0
Modular network for high accuracy object detection—0
A Study of the Tasks and Models in Machine Reading Comprehension—0
Automatic phantom test pattern classification through transfer learning with deep neural networks—0
AutoFCL: Automatically Tuning Fully Connected Layers for Handling Small DatasetCode0
Transfer Learning using Neural Ordinary Differential Equations—0
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications—0
Heterogeneous Transfer Learning in Ensemble Clustering—0
GTNet: Generative Transfer Network for Zero-Shot Object DetectionCode0
A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration—0
Durocmien: A deep framework for duroc skeleton extraction in constraint environment—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