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ORCID30th ICANN 2021: Bratislava, Slovakia - Part II
- Igor Farkas
, Paolo Masulli, Sebastian Otte, Stefan Wermter:
Artificial Neural Networks and Machine Learning - ICANN 2021 - 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part II. Lecture Notes in Computer Science 12892, Springer 2021, ISBN 978-3-030-86339-5
Computer Vision and Object Detection
- Junliang Chen, Weizeng Lu, Linlin Shen:
Selective Multi-scale Learning for Object Detection. 3-14 - Changsheng Liu, Yuan Huang, Yadong Mu, Xiaoming Yu:
DRENet: Giving Full Scope to Detection and Regression-Based Estimation for Video Crowd Counting. 15-27 - Jairo Lucas de Moraes, Jorcy de Oliveira Neto, Jacson Rodrigues Correia da Silva, Thiago M. Paixão, Claudine Badue, Thiago Oliveira-Santos, Alberto F. De Souza:
Sisfrutos Papaya: A Dataset for Detection and Classification of Diseases in Papaya. 28-39 - Francesco Manigrasso, Filomeno Davide Miro, Lia Morra, Fabrizio Lamberti:
Faster-LTN: A Neuro-Symbolic, End-to-End Object Detection Architecture. 40-52 - Ying Shi, Jun Sang, Jinghan Tan, Zhongyuan Wu, Bin Cai, Nong Sang:
GC-MRNet: Gated Cascade Multi-stage Regression Network for Crowd Counting. 53-66 - Caixia Liu, Dehui Kong, Shaofan Wang, Jinghua Li, Baocai Yin:
Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a Single Depth View. 67-79 - Jie Lei, Zhao Liu, Zeyu Zou, Tong Li, Juan Xu, Zunlei Feng, Ronghua Liang:
Facial Expression Recognition by Expression-Specific Representation Swapping. 80-91 - Zhuolin Zheng, Yinzhang Ding, Xiaotian Tang, Yu Cai, Dongxiao Li, Ming Zhang, Hongyang Xie, Xuanfu Li:
Iterative Error Removal for Time-of-Flight Depth Imaging. 92-105 - Wenjie Zhang, Zhi Wang:
Blurred Image Recognition: A Joint Motion Deblurring and Classification Loss-Aware Approach. 106-117 - Wenjie Chen, Shuang Ran, Tian Wang, Lihong Cao:
Learning How to Zoom In: Weakly Supervised ROI-Based-DAM for Fine-Grained Visual Classification. 118-130
Convolutional Neural Networks and Kernel Methods
- Mats L. Richter, Wolf Byttner, Ulf Krumnack, Anna Wiedenroth, Ludwig Schallner, Justin Shenk:
(Input) Size Matters for CNN Classifiers. 133-144 - Yuekai Zhao, Jianzhuang Lu, Xiaowen Chen:
Accelerating Depthwise Separable Convolutions with Vector Processor. 145-156 - Nidhi Goyal, Niharika Sachdeva, Anmol Goel, Jushaan Singh Kalra, Ponnurangam Kumaraguru:
KCNet: Kernel-Based Canonicalization Network for Entities in Recruitment Domain. 157-169 - Hao-Yuan Chang, Kang L. Wang:
Deep Unitary Convolutional Neural Networks. 170-181
Deep Learning and Optimization I
- Achraf Bennis, Sandrine Mouysset, Mathieu Serrurier:
DPWTE: A Deep Learning Approach to Survival Analysis Using a Parsimonious Mixture of Weibull Distributions. 185-196 - Thomas Pierrot, Nicolas Perrin-Gilbert, Olivier Sigaud:
First-Order and Second-Order Variants of the Gradient Descent in a Unified Framework. 197-208 - Nengli Lim, Yueqin Li:
Bayesian Optimization for Backpropagation in Monte-Carlo Tree Search. 209-221 - Paul Caillon, Christophe Cerisara:
Growing Neural Networks Achieve Flatter Minima. 222-234 - Alexander Kovalenko, Pavel Kordík, Magda Friedjungová:
Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks. 235-247 - Yongguang Wang, Huobin Tan, Shuzhen Yao:
Curved SDE-Net Leads to Better Generalization for Uncertainty Estimates of DNNs. 248-259 - Koushik Biswas, Sandeep Kumar, Shilpak Banerjee, Ashish Kumar Pandey:
EIS - Efficient and Trainable Activation Functions for Better Accuracy and Performance. 260-272
Deep Learning and Optimization II
- Masanari Kimura:
Why Mixup Improves the Model Performance. 275-286 - Takumi Yamaguchi, Masahiro Murakawa:
Mixup Gamblers: Learning to Abstain with Auto-Calibrated Reward for Mixed Samples. 287-294 - Tobias Uelwer, Tobias Hoffmann, Stefan Harmeling:
Non-iterative Phase Retrieval with Cascaded Neural Networks. 295-306 - Guihua Tao, Wentao Rong, Wanlin Weng, Tingting Dan, Bin Zhang, Hongmin Cai:
Incorporating Discrete Wavelet Transformation Decomposition Convolution into Deep Network to Achieve Light Training. 307-318 - Jingyun Jia, Philip K. Chan:
MMF: A Loss Extension for Feature Learning in Open Set Recognition. 319-331 - Daniel Bacaicoa-Barber, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Jesús Cid-Sueiro:
On the Selection of Loss Functions Under Known Weak Label Models. 332-343
Distributed and Continual Learning
- Ya-nan Han, Jian-wei Liu, Bing-biao Xiao, Xin-Tan Wang, Xionglin Luo:
Bilevel Online Deep Learning in Non-stationary Environment. 347-358 - Jian Zhao, Xin Wu, Yan Zhang, Yu Wu, Zhi Wang:
A Blockchain Based Decentralized Gradient Aggregation Design for Federated Learning. 359-371 - Yi Han, Shanika Karunasekera, Christopher Leckie:
Continual Learning for Fake News Detection from Social Media. 372-384 - Quentin Jodelet, Xin Liu, Tsuyoshi Murata:
Balanced Softmax Cross-Entropy for Incremental Learning. 385-396 - Diana Benavides Prado, Chathura Wanigasekara, Akshya Swain:
Generalised Controller Design Using Continual Learning. 397-408 - Kyra Ahrens, Fares Abawi, Stefan Wermter:
DRILL: Dynamic Representations for Imbalanced Lifelong Learning. 409-420 - Zhiyi Chen, Tong Lin:
Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learning. 421-432
Explainable Methods
- Cilie W. Feldager, Søren Hauberg, Lars Kai Hansen:
Spontaneous Symmetry Breaking in Data Visualization. 435-446 - Reza Marzban, Christopher Crick:
Deep NLP Explainer: Using Prediction Slope to Explain NLP Models. 447-458 - Maximus Mutschler, Andreas Zell:
Empirically Explaining SGD from a Line Search Perspective. 459-471 - Grégory Bourguin, Arnaud Lewandowski, Mourad Bouneffa, Adeel Ahmad:
Towards Ontologically Explainable Classifiers. 472-484
Few-shot Learning
- Yuqing Hu, Vincent Gripon, Stéphane Pateux:
Leveraging the Feature Distribution in Transfer-Based Few-Shot Learning. 487-499 - Gianfranco Mauro, Mateusz Chmurski, Muhammad Arsalan, Mariusz Zubert, Vadim Issakov:
One-Shot Meta-learning for Radar-Based Gesture Sequences Recognition. 500-511 - Xin Wang, Shouhong Wan, Peiquan Jin:
Few-Shot Learning with Random Erasing and Task-Relevant Feature Transforming. 512-524 - Sarah Fabi, Sebastian Otte, Martin V. Butz:
Fostering Compositionality in Latent, Generative Encodings to Solve the Omniglot Challenge. 525-536 - Zheng Chen, Yunchen Zhang:
Better Few-Shot Text Classification with Pre-trained Language Model. 537-548
Generative Adversarial Networks
- Xuyang Peng, Weifeng Liu, Baodi Liu, Kai Zhang, Xiaoping Lu, Yicong Zhou:
Leveraging GANs via Non-local Features. 551-562 - Kaifeng Zhang:
On Mode Collapse in Generative Adversarial Networks. 563-574 - Daniel Vasata, Tomás Halama, Magda Friedjungová:
Image Inpainting Using Wasserstein Generative Adversarial Imputation Network. 575-586 - Ara Abigail E. Ambita, Eujene Nikka V. Boquio, Prospero C. Naval:
COViT-GAN: Vision Transformer forCOVID-19 Detection in CT Scan Imageswith Self-Attention GAN forDataAugmentation. 587-598 - Nuha Aldausari, Arcot Sowmya, Nadine Marcus, Gelareh Mohammadi:
PhonicsGAN: Synthesizing Graphical Videos from Phonics Songs. 599-610 - Liang Nie, Wenxin Yu, Xuewen Zhang, Siyuan Li, Ning Jiang, Zhiqiang Zhang:
A Progressive Image Inpainting Algorithm with a Mask Auto-update Branch. 611-622 - Hoda Shajari, Jaemoon Lee, Sanjay Ranka, Anand Rangarajan:
Hybrid Generative Models for Two-Dimensional Datasets. 623-636 - Luqi Gong, Chao Li, Hailong Hong, Hui Zhu, Tangwen Qian, Yongjun Xu:
Towards Compressing Efficient Generative Adversarial Networks for Image Translation via Pruning and Distilling. 637-647
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