ImitAL: Learned Active Learning Strategy on Synthetic Data
Fuente:
arXiv
Saved in:
| Main Authors: | Gonsior, Julius, Thiele, Maik, Lehner, Wolfgang |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models
by: Gonsior, Julius, et al.
Published: (2022)
by: Gonsior, Julius, et al.
Published: (2022)
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid
by: Gonsior, Julius, et al.
Published: (2025)
by: Gonsior, Julius, et al.
Published: (2025)
ImitDiff: Transferring Foundation-Model Priors for Distraction Robust Visuomotor Policy
by: Dong, Yuhang, et al.
Published: (2025)
by: Dong, Yuhang, et al.
Published: (2025)
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey
by: Romberg, Julia, et al.
Published: (2025)
by: Romberg, Julia, et al.
Published: (2025)
Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
by: von Kügelgen, Julius
Published: (2024)
by: von Kügelgen, Julius
Published: (2024)
Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
by: Kelleher, John D., et al.
Published: (2025)
by: Kelleher, John D., et al.
Published: (2025)
Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data
by: Zhang, Jiancheng, et al.
Published: (2025)
by: Zhang, Jiancheng, et al.
Published: (2025)
Pre-training with Synthetic Data Helps Offline Reinforcement Learning
by: Wang, Zecheng, et al.
Published: (2023)
by: Wang, Zecheng, et al.
Published: (2023)
Synthetic Data Aided Federated Learning Using Foundation Models
by: Abacha, Fatima, et al.
Published: (2024)
by: Abacha, Fatima, et al.
Published: (2024)
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
by: Wagner, Stefan Sylvius, et al.
Published: (2024)
by: Wagner, Stefan Sylvius, et al.
Published: (2024)
Data-Driven Optimization of EV Charging Station Placement Using Causal Discovery
by: Junker, Julius Stephan, et al.
Published: (2025)
by: Junker, Julius Stephan, et al.
Published: (2025)
BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learning
by: Huseljic, Denis, et al.
Published: (2026)
by: Huseljic, Denis, et al.
Published: (2026)
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification
by: Núñez, José Fernando, et al.
Published: (2024)
by: Núñez, José Fernando, et al.
Published: (2024)
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
by: Margraf, Valentin, et al.
Published: (2024)
by: Margraf, Valentin, et al.
Published: (2024)
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
by: Huang, Chun-Yin, et al.
Published: (2024)
by: Huang, Chun-Yin, et al.
Published: (2024)
Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation
by: Sikder, Md Fahim, et al.
Published: (2024)
by: Sikder, Md Fahim, et al.
Published: (2024)
Not All Synthetic Data Is Yours to Learn From
by: Alemohammad, Sina, et al.
Published: (2026)
by: Alemohammad, Sina, et al.
Published: (2026)
A Pre-trained Data Deduplication Model based on Active Learning
by: Shi, Haochen, et al.
Published: (2023)
by: Shi, Haochen, et al.
Published: (2023)
Budget-constrained Active Learning to Effectively De-censor Survival Data
by: Parsaee, Ali, et al.
Published: (2025)
by: Parsaee, Ali, et al.
Published: (2025)
Forget the Data and Fine-Tuning! Just Fold the Network to Compress
by: Wang, Dong, et al.
Published: (2025)
by: Wang, Dong, et al.
Published: (2025)
Benchmarking Active Learning for NILM
by: Patel, Dhruv, et al.
Published: (2024)
by: Patel, Dhruv, et al.
Published: (2024)
Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
by: Cazaux, Hugo, et al.
Published: (2026)
by: Cazaux, Hugo, et al.
Published: (2026)
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning
by: Liu, Xin, et al.
Published: (2024)
by: Liu, Xin, et al.
Published: (2024)
Improving Machine Learning Performance with Synthetic Augmentation
by: Sohm, Mel, et al.
Published: (2026)
by: Sohm, Mel, et al.
Published: (2026)
ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
by: Melikidze, Davit, et al.
Published: (2026)
by: Melikidze, Davit, et al.
Published: (2026)
Active Learning for Continual Learning: Keeping the Past Alive in the Present
by: Park, Jaehyun, et al.
Published: (2025)
by: Park, Jaehyun, et al.
Published: (2025)
Learning from Synthetic Data Improves Multi-hop Reasoning
by: Kabra, Anmol, et al.
Published: (2026)
by: Kabra, Anmol, et al.
Published: (2026)
SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry
by: Jain, Ankita Kumari, et al.
Published: (2024)
by: Jain, Ankita Kumari, et al.
Published: (2024)
Transductive Active Learning: Theory and Applications
by: Hübotter, Jonas, et al.
Published: (2024)
by: Hübotter, Jonas, et al.
Published: (2024)
Neural Active Learning Beyond Bandits
by: Ban, Yikun, et al.
Published: (2024)
by: Ban, Yikun, et al.
Published: (2024)
ALVIN: Active Learning Via INterpolation
by: Korakakis, Michalis, et al.
Published: (2024)
by: Korakakis, Michalis, et al.
Published: (2024)
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Data
by: Cribeiro-Ramallo, Jose, et al.
Published: (2024)
by: Cribeiro-Ramallo, Jose, et al.
Published: (2024)
Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies
by: Liu, Qi, et al.
Published: (2024)
by: Liu, Qi, et al.
Published: (2024)
Enhancing Machine Learning for Imbalanced Medical Data: A Quantum-Inspired Approach to Synthetic Oversampling (QI-SMOTE)
by: Kashtriya, Vikas, et al.
Published: (2025)
by: Kashtriya, Vikas, et al.
Published: (2025)
Synthetic Data Privacy Metrics
by: Steier, Amy, et al.
Published: (2025)
by: Steier, Amy, et al.
Published: (2025)
Parametric Neural Amp Modeling with Active Learning
by: Grötschla, Florian, et al.
Published: (2025)
by: Grötschla, Florian, et al.
Published: (2025)
Does Deep Active Learning Work in the Wild?
by: Ren, Simiao, et al.
Published: (2023)
by: Ren, Simiao, et al.
Published: (2023)
Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies
by: Wachter, Paul, et al.
Published: (2025)
by: Wachter, Paul, et al.
Published: (2025)
DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning
by: Lu, Yao, et al.
Published: (2025)
by: Lu, Yao, et al.
Published: (2025)
Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies
by: Chen, Jiaqi, et al.
Published: (2025)
by: Chen, Jiaqi, et al.
Published: (2025)
Similar Items
-
To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models
by: Gonsior, Julius, et al.
Published: (2022) -
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid
by: Gonsior, Julius, et al.
Published: (2025) -
ImitDiff: Transferring Foundation-Model Priors for Distraction Robust Visuomotor Policy
by: Dong, Yuhang, et al.
Published: (2025) -
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey
by: Romberg, Julia, et al.
Published: (2025) -
Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
by: von Kügelgen, Julius
Published: (2024)