Category: ICCV
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Domain-adaptive deep network compression
Marc Masana, Joost van de Weijer, Luis Herranz, Andrew D. Bagdanov, Jose M Alvarez Read Full Paper → Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller […]
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Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB
Aitor Alvarez-Gila, Joost van de Weijer, Estibaliz Garrote Read Full Paper → Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to […]
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RankIQA: Learning from Rankings for No-reference Image Quality Assessment
Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov Read Full Paper → We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative […]
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Domain-adaptive deep network compression
Marc Masana, Joost van de Weijer, Luis Herranz, Andrew D. Bagdanov, Jose M Alvarez Read Full Paper → Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller […]