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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7401–7425 of 10307 papers

TitleStatusHype
An Effective Scheme for Maize Disease Recognition based on Deep Networks—0
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation—0
MAPLE: Microprocessor A Priori for Latency Estimation—0
An Effective End-to-End Solution for Multimodal Action Recognition—0
Smile detection in the wild based on transfer learning—0
Mapping probability word problems to executable representations—0
SMILE: Self-Distilled MIxup for Efficient Transfer LEarning—0
Anchor-based Bilingual Word Embeddings for Low-Resource Languages—0
MarineDet: Towards Open-Marine Object Detection—0
Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation—0
Markerless tracking of user-defined features with deep learning—0
An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic—0
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model—0
Marvelous Agglutinative Language Effect on Cross Lingual Transfer Learning—0
Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-Identification—0
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning—0
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning—0
Smoothness Adaptive Hypothesis Transfer Learning—0
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets—0
An Automated Deep Learning Approach for Bacterial Image Classification—0
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization—0
Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation—0
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild—0
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges—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