Search Results for author: Jaekwon Yoo

Found 2 papers, 1 papers with code

Continual Learning for End-to-End ASR by Averaging Domain Experts

no code implementations12 May 2023 Peter Plantinga, Jaekwon Yoo, Chandra Dhir

Our experiments show that a simple linear interpolation of several models' parameters, each fine-tuned from the same generalist model, results in a single model that performs well on all tested data.

Automatic Speech Recognition Continual Learning +2

Temporal Attentive Alignment for Large-Scale Video Domain Adaptation

5 code implementations ICCV 2019 Min-Hung Chen, Zsolt Kira, Ghassan AlRegib, Jaekwon Yoo, Ruxin Chen, Jian Zheng

Finally, we propose Temporal Attentive Adversarial Adaptation Network (TA3N), which explicitly attends to the temporal dynamics using domain discrepancy for more effective domain alignment, achieving state-of-the-art performance on four video DA datasets (e. g. 7. 9% accuracy gain over "Source only" from 73. 9% to 81. 8% on "HMDB --> UCF", and 10. 3% gain on "Kinetics --> Gameplay").

Unsupervised Domain Adaptation

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