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 5401–5425 of 10307 papers

TitleStatusHype
Low-Resource Vision Challenges for Foundation Models—0
Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches—0
Low-supervision urgency detection and transfer in short crisis messages—0
Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks—0
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment—0
LS-DYNA Machine Learning-based Multiscale Method for Nonlinear Modeling of Short Fiber-Reinforced Composites—0
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity—0
LSTM knowledge transfer for HRV-based sleep staging—0
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches—0
CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans—0
LuoJiaHOG: A Hierarchy Oriented Geo-aware Image Caption Dataset for Remote Sensing Image-Text Retrival—0
M2D: A Multi-modal Framework for Automatic Medical Diagnosis—0
M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation—0
M^2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension—0
M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification—0
Machine-assisted annotation of forensic imagery—0
Machine Intelligence for Outcome Predictions of Trauma Patients During Emergency Department Care—0
Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study—0
COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features—0
Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review—0
Machine Learning based Post Processing Artifact Reduction in HEVC Intra Coding—0
Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks—0
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review—0
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules—0
Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study—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