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 2201–2225 of 10307 papers

TitleStatusHype
CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning—0
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation—0
An Efficient Approach to Detecting Lung Nodules Using Swin Transformer—0
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction—0
Data Instance Prior for Transfer Learning in GANs—0
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model—0
Bypassing Optimization Complexity through Transfer Learning & Deep Neural Nets for Speech Intelligibility Improvement—0
Burgers' pinns with implicit euler transfer learning—0
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation—0
Advances and Challenges in Meta-Learning: A Technical Review—0
Buildings Classification using Very High Resolution Satellite Imagery—0
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning—0
Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures—0
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets—0
Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery—0
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations—0
Building medical image classifiers with very limited data using segmentation networks—0
Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Building Height Prediction with Instance Segmentation—0
Building Efficient Lightweight CNN Models—0
Annotation Techniques for Judo Combat Phase Classification from Tournament Footage—0
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion—0
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach—0
Building a Question and Answer System for News Domain—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