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 3776–3800 of 10307 papers

TitleStatusHype
Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer LearningCode0
Risk of Transfer Learning and its Applications in Finance—0
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models—0
Robust Fine-Tuning of Vision-Language Models for Domain GeneralizationCode0
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer—0
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection—0
Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics—0
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?—0
Determination of droplet size from wide-angle light scattering image data using convolutional neural networks—0
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery—0
IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue SystemsCode0
Scattering Vision Transformer: Spectral Mixing Matters—0
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning—0
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms—0
M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object DetectionCode0
ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection—0
TLMCM Network for Medical Image Hierarchical Multi-Label Classification—0
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations—0
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning—0
Investigating Relative Performance of Transfer and Meta Learning—0
Self-Supervised Pre-Training for Precipitation Post-Processor—0
MENTOR: Human Perception-Guided Pretraining for Increased Generalization—0
Topological Learning for Motion Data via Mixed CoordinatesCode0
On consequences of finetuning on data with highly discriminative features—0
Show:102550
← PrevPage 152 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