Memory replay gans: Learning to generate new categories without forgetting C Wu, L Herranz, X Liu, J Van De Weijer, B Raducanu Advances in neural information processing systems 31, 2018 | 420 | 2018 |
Transferring gans: generating images from limited data Y Wang, C Wu, L Herranz, J Van de Weijer, A Gonzalez-Garcia, ... Proceedings of the European conference on computer vision (ECCV), 218-234, 2018 | 324 | 2018 |
Semantic drift compensation for class-incremental learning L Yu, B Twardowski, X Liu, L Herranz, K Wang, Y Cheng, S Jui, J Weijer Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 298 | 2020 |
Rotate your networks: Better weight consolidation and less catastrophic forgetting X Liu, M Masana, L Herranz, J Van de Weijer, AM Lopez, AD Bagdanov 2018 24th International Conference on Pattern Recognition (ICPR), 2262-2268, 2018 | 265 | 2018 |
Scene recognition with cnns: objects, scales and dataset bias L Herranz, S Jiang, X Li Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 238 | 2016 |
Generalized source-free domain adaptation S Yang, Y Wang, J Van De Weijer, L Herranz, S Jui Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 234 | 2021 |
Exploiting the intrinsic neighborhood structure for source-free domain adaptation S Yang, J van de Weijer, L Herranz, S Jui Advances in neural information processing systems 34, 29393-29405, 2021 | 220 | 2021 |
Minegan: effective knowledge transfer from gans to target domains with few images Y Wang, A Gonzalez-Garcia, D Berga, L Herranz, FS Khan, J Weijer Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 190 | 2020 |
Generative feature replay for class-incremental learning X Liu, C Wu, M Menta, L Herranz, B Raducanu, AD Bagdanov, S Jui, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 138 | 2020 |
Being a supercook: Joint food attributes and multimodal content modeling for recipe retrieval and exploration W Min, S Jiang, J Sang, H Wang, X Liu, L Herranz IEEE transactions on multimedia 19 (5), 1100-1113, 2016 | 119 | 2016 |
LIUM-CVC submissions for WMT17 multimodal translation task O Caglayan, W Aransa, A Bardet, M García-Martínez, F Bougares, ... arXiv preprint arXiv:1707.04481, 2017 | 111 | 2017 |
Geolocalized modeling for dish recognition R Xu, L Herranz, S Jiang, S Wang, X Song, R Jain IEEE transactions on multimedia 17 (8), 1187-1199, 2015 | 108 | 2015 |
Variable rate deep image compression with modulated autoencoder F Yang, L Herranz, J Van De Weijer, JAI Guitián, AM López, MG Mozerov IEEE Signal Processing Letters 27, 331-335, 2020 | 90 | 2020 |
Unsupervised domain adaptation without source data by casting a bait S Yang, Y Wang, J Van De Weijer, L Herranz, S Jui arXiv preprint arXiv:2010.12427 1 (2), 5, 2020 | 90 | 2020 |
Depth CNNs for RGB-D scene recognition: Learning from scratch better than transferring from RGB-CNNs X Song, L Herranz, S Jiang Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 88 | 2017 |
Domain-adaptive deep network compression M Masana, J Van De Weijer, L Herranz, AD Bagdanov, JM Alvarez Proceedings of the IEEE International Conference on Computer Vision, 4289-4297, 2017 | 75 | 2017 |
Multi-scale multi-feature context modeling for scene recognition in the semantic manifold X Song, S Jiang, L Herranz IEEE Transactions on Image Processing 26 (6), 2721-2735, 2017 | 74 | 2017 |
Slimmable compressive autoencoders for practical neural image compression F Yang, L Herranz, Y Cheng, MG Mozerov Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 67 | 2021 |
Modeling restaurant context for food recognition L Herranz, S Jiang, R Xu IEEE Transactions on Multimedia 19 (2), 430-440, 2016 | 62 | 2016 |
Learning effective RGB-D representations for scene recognition X Song, S Jiang, L Herranz, C Chen IEEE Transactions on Image Processing 28 (2), 980-993, 2018 | 56 | 2018 |