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 5201–5225 of 10307 papers

TitleStatusHype
Regularized Soft Actor-Critic for Behavior Transfer Learning—0
Design Perspectives of Multitask Deep Learning Models and Applications—0
SmartFPS: Neural Network based Wireless-inertial fusion positioning system—0
Self-supervised similarity models based on well-logging data—0
Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide ImagesCode0
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning—0
Habitat classification from satellite observations with sparse annotations—0
YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)—0
Transfer learning for self-supervised, blind-spot seismic denoising—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Transformer-Based Microbubble Localization—0
The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022—0
COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs—0
Joint PMD Tracking and Nonlinearity Compensation with Deep Neural Networks—0
Extreme Multi-Domain, Multi-Task Learning With Unified Text-to-Text Transfer TransformersCode0
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts—0
One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation—0
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries—0
Towards Task-Prioritized Policy Composition—0
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-LearningCode0
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning—0
Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features—0
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods—0
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association LearningCode0
An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer LearningCode0
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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