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Ben Kadlec

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Research Papers

Faster Neural Networks Straight from JPEG

L. Gueguen, A. Sergeev, B. Kadlec, R. Liu, J. Yosinski
The simple, elegant approach of training convolutional neural networks (CNNs) directly from RGB pixels has enjoyed overwhelming empirical success. But can more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. [...] [PDF]
Advances in Neural Information Processing Systems (NeurIPS), 2018

Uber-Text: A Large-Scale Dataset for Optical Character Recognition from Street-Level Imagery

Y. Zhang, L. Gueguen, I. Zharkov, P. Zhang, K. Seifert, B. Kadlec
Optical Character Recognition (OCR) approaches have been widely advanced in recent years thanks to the resurgence of deep learning. The state-of-the-art models are mainly trained on the datasets consisting of the constrained scenes. Detecting and recognizing text from the real-world images remains a technical challenge. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

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