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 8251–8275 of 10307 papers

TitleStatusHype
Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
A general approach to bridge the reality-gap—0
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes—0
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning—0
On Adversarial Robustness of Language Models in Transfer Learning—0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
STemGAN: spatio-temporal generative adversarial network for video anomaly detection—0
Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge—0
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor—0
On Conditional and Compositional Language Model Differentiable Prompting—0
On consequences of finetuning on data with highly discriminative features—0
Step Out and Seek Around: On Warm-Start Training with Incremental Data—0
Age and Gender Prediction using Deep CNNs and Transfer Learning—0
On-Device Transfer Learning for Personalising Psychological Stress Modelling using a Convolutional Neural Network—0
One4all User Representation for Recommender Systems in E-commerce—0
Stiff Transfer Learning for Physics-Informed Neural Networks—0
On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation—0
On effects of Knowledge Distillation on Transfer Learning—0
AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive Outcome for Infant Brain Injury—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them—0
One for Many: Transfer Learning for Building HVAC Control—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
One Model to Rule them All: Towards Zero-Shot Learning for Databases—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