Transfer Learning for Olfactory Object Detection

Zinnen M, Madhu P, Bell P, Maier A, Christlein V (2022)

Publication Language: English

Publication Type: Conference contribution, Abstract of lecture

Publication year: 2022

Series: Digitial Humanities

Book Volume: 2022

Pages Range: 409-413

Conference Proceedings Title: Digital Humanities 2022 Conference Abstracts

Event location: Tokyo, Japan, Online JP


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We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining. We find that including an additional stage of object-detection pretraining can increase the detection performance considerably. While our experiments suggest that style similarities between pre-training and target datasets are less important than matching categories, further experiments are needed to verify this hypothesis.

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How to cite


Zinnen, M., Madhu, P., Bell, P., Maier, A., & Christlein, V. (2022, July). Transfer Learning for Olfactory Object Detection. Paper presentation at Digital Humanities 2022, Tokyo, Japan, Online, JP.


Zinnen, Mathias, et al. "Transfer Learning for Olfactory Object Detection." Presented at Digital Humanities 2022, Tokyo, Japan, Online Ed. Alliance of Digital Humanities Organizations, 2022.

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