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 6751–6800 of 10307 papers

TitleStatusHype
Scalable Weight Reparametrization for Efficient Transfer Learning—0
Scalarization for Multi-Task and Multi-Domain Learning at Scale—0
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
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset—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
Script Parsing with Hierarchical Sequence Modelling—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
Secost: Sequential co-supervision for large scale weakly labeled audio event detection—0
Secure Federated Transfer Learning—0
SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection—0
Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets—0
Security of Deep Learning Methodologies: Challenges and Opportunities—0
Security Vulnerability Detection Using Deep Learning Natural Language Processing—0
Seed Phenotyping on Neural Networks using Domain Randomization and Transfer Learning—0
Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression—0
SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation—0
Segmentation Framework for Heat Loss Identification in Thermal Images: Empowering Scottish Retrofitting and Thermographic Survey Companies—0
Segmentation of Mental Foramen in Orthopantomographs: A Deep Learning Approach—0
Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder—0
Segmenting across places: The need for fair transfer learning with satellite imagery—0
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models—0
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing—0
SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment—0
Selective Token Generation for Few-shot Language Modeling—0
Selective transfer learning with adversarial training for stock movement prediction—0
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation—0
Self-adaptive Multi-task Particle Swarm Optimization—0
Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images—0
Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation—0
Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning—0
Self-Composing Policies for Scalable Continual Reinforcement Learning—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Self-Controlled Dynamic Expansion Model for Continual Learning—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—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