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 6951–6975 of 10307 papers

TitleStatusHype
Imponderous Net for Facial Expression Recognition in the Wild—0
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification—0
Multi-source Transfer Learning with Ensemble for Financial Time Series Forecasting—0
When Few-Shot Learning Meets Video Object Detection—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data—0
SMILE: Self-Distilled MIxup for Efficient Transfer LEarning—0
Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks—0
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution—0
Active Multitask Learning with Committees—0
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene SegmentationCode0
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning—0
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures—0
Fisher Task Distance and Its Application in Neural Architecture SearchCode0
Student Network Learning via Evolutionary Knowledge Distillation—0
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural NetworksCode0
Channel Scaling: A Scale-and-Select Approach for Transfer Learning—0
Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets—0
A Modular and Unified Framework for Detecting and Localizing Video Anomalies—0
Visualization of Deep Transfer Learning In SAR Imagery—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
ThanosNet: A Novel Trash Classification Method Using MetadataCode0
A first step towards automated species recognition from camera trap images of mammals using AI in a European temperate forest—0
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition SystemsCode0
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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