S-Extension Patch: A simple and efficient way to extend an object detection model
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Abstract (arXiv)
While building convolutional network-based systems, the toll it takes to train the network is something that cannot be ignored. In cases where we need to append additional capabilities to the existing model, the attention immediately goes towards retraining techniques. In this paper, I show how to leverage knowledge about the dataset to append the class faster while maintaining the speed of inference as well as the accuracies; while reducing the amount of time and data required. The method can extend a class in the existing object detection model in 1/10th of the time compared to the other existing methods. S-Extension patch not only offers faster training but also speed and ease of adaptation, as it can be appended to any existing system, given it fulfills the similarity threshold condition.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 16 references that are in the map. First posted on arXiv 6 Oct 2021.
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7 of 16 references.
References · 7
- 2015Distilling the Knowledge in a Neural Networkcited by 10,324
- 2016Learning without Forgettingcited by 2,496
- 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 811
- 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
- 2016Less-forgetting Learning in Deep Neural Networkscited by 97
- 2019Growing a Brain: Fine-Tuning by Increasing Model Capacitycited by 43
- 2019Why do These Match? Explaining the Behavior of Image Similarity Modelscited by 10
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