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 8701–8725 of 10307 papers

TitleStatusHype
Transfer Learning for Health-related Twitter DataCode0
Few-Shot Meta-Denoising—0
Interior Object Detection and Color Harmonization—0
Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation—0
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods—0
Zero-shot transfer for implicit discourse relation classificationCode0
Towards More Accurate Automatic Sleep Staging via Deep Transfer LearningCode0
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets—0
Learnable Parameter SimilarityCode0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images—0
Semisupervised Adversarial Neural Networks for Cyber Security Transfer Learning—0
Knowledge transfer in deep block-modular neural networks—0
Gait recognition via deep learning of the center-of-pressure trajectory—0
Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task—0
Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?—0
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications—0
Automatic detection of rare pathologies in fundus photographs using few-shot learning—0
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition—0
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare—0
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs—0
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach—0
Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural NetworksCode0
Post-Earthquake Assessment of Buildings Using Deep Learning—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