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 7376–7400 of 10307 papers

TitleStatusHype
Making deep neural networks work for medical audio: representation, compression and domain adaptation—0
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning—0
Making Person Search Enjoy the Merits of Person Re-identification—0
Malaria Cell Detection Using Deep Neural Networks—0
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application—0
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework—0
An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks—0
Malware Classification Using Deep Boosted Learning—0
Malware Classification Using Transfer Learning—0
An Optimized Ensemble Deep Learning Model For Brain Tumor Classification—0
Mamba-Adaptor: State Space Model Adaptor for Visual Recognition—0
Mammography Dual View Mass Correspondence—0
MAMOC: MRI Motion Correction via Masked Autoencoding—0
Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes—0
Mandarin-English Code-Switching Speech Recognition System for Specific Domain—0
Manifold Alignment with Label Information—0
A Collaborative Transfer Learning Framework for Cross-domain Recommendation—0
SMC Faster R-CNN: Toward a scene-specialized multi-object detector—0
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation—0
Manifold Learning via Foliations and Knowledge Transfer—0
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis—0
Manufacturing Dispatching using Reinforcement and Transfer Learning—0
An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild—0
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification—0
Many-Task Federated Fine-Tuning via Unified Task Vectors—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