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 6651–6675 of 10307 papers

TitleStatusHype
ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector—0
Scaling Language Model Size in Cross-Device Federated Learning—0
Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions—0
Scaling Laws for Data-Efficient Visual Transfer Learning—0
Scaling Laws for Downstream Task Performance of Large Language Models—0
Scaling Laws for Transfer—0
Scattering Vision Transformer: Spectral Mixing Matters—0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
Scene-adaptive and Region-aware Multi-modal Prompt for Open Vocabulary Object Detection—0
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation—0
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset—0
BIGSAGE: unsupervised inductive representation learning of graph via bi-attended sampling and global-biased aggregating—0
Schrödinger's Tree -- On Syntax and Neural Language Models—0
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks—0
Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning—0
SciWING– A Software Toolkit for Scientific Document Processing—0
Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19—0
Bi-level Unbalanced Optimal Transport for Partial Domain Adaptation—0
Script Parsing with Hierarchical Sequence Modelling—0
Bilinear classifiers for visual recognition—0
Rotation-equivariant Graph Neural Networks for Learning Glassy Liquids Representations—0
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities—0
SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild—0
SEALion: a Framework for Neural Network Inference on Encrypted Data—0
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits—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