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 5701–5725 of 10307 papers

TitleStatusHype
AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages—0
Wound Severity Classification using Deep Neural Network—0
STRATA: Word Boundaries & Phoneme Recognition From Continuous Urdu Speech using Transfer Learning, Attention, & Data Augmentation—0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm—0
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes—0
Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification—0
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking—0
Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling—0
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach—0
Transfer Learning for Autonomous Chatter Detection in Machining—0
Fake news detection using parallel BERT deep neural networks—0
Segmenting across places: The need for fair transfer learning with satellite imagery—0
A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification—0
IDPG: An Instance-Dependent Prompt Generation Method—0
Does Robustness on ImageNet Transfer to Downstream Tasks?—0
Blockchain as an Enabler for Transfer Learning in Smart Environments—0
Marvelous Agglutinative Language Effect on Cross Lingual Transfer Learning—0
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model—0
A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis—0
Pneumonia Detection in Chest X-Rays using Neural Networks—0
Forecasting new diseases in low-data settings using transfer learningCode0
Implementing a Real-Time, YOLOv5 based Social Distancing Measuring System for Covid-19—0
EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware PseudocoloringCode0
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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