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 8176–8200 of 10307 papers

TitleStatusHype
Two-Level Attention-based Fusion Learning for RGB-D Face RecognitionCode0
Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy—0
The Utility of Feature Reuse: Transfer Learning in Data-Starved RegimesCode0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Adapted tree boosting for Transfer Learning—0
LEEP: A New Measure to Evaluate Transferability of Learned Representations—0
A Free-Energy Principle for Representation Learning—0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance NormalizationCode0
Towards Learning a Universal Non-Semantic Representation of SpeechCode0
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning—0
How Transferable are the Representations Learned by Deep Q Agents?—0
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks—0
Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning—0
Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes—0
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models—0
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions—0
A Systematic Comparison of Architectures for Document-Level Sentiment ClassificationCode0
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
Efficient Deep Reinforcement Learning via Adaptive Policy TransferCode0
Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths—0
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN—0
SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection—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