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 5451–5475 of 10307 papers

TitleStatusHype
Mamba-Adaptor: State Space Model Adaptor for Visual Recognition—0
Mammography Dual View Mass Correspondence—0
MAMOC: MRI Motion Correction via Masked Autoencoding—0
Mandarin-English Code-Switching Speech Recognition System for Specific Domain—0
Manifold Alignment with Label Information—0
Manifold Learning via Foliations and Knowledge Transfer—0
Manufacturing Dispatching using Reinforcement and Transfer Learning—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
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation—0
MAPLE: Microprocessor A Priori for Latency Estimation—0
Mapping probability word problems to executable representations—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
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
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets—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
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges—0
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning—0
Master Thesis: Neural Sign Language Translation by Learning Tokenization—0
Matching Text and Audio Embeddings: Exploring Transfer-learning Strategies for Language-based Audio Retrieval—0
Show:102550
← PrevPage 219 of 413Next →
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