Poster "convolutional neural networks" Papers
23 papers found
Conference
Adaptive Rectangular Convolution for Remote Sensing Pansharpening
Xueyang Wang, Zhixin Zheng, Jiandong Shao et al.
As large as it gets – Studying Infinitely Large Convolutions via Neural Implicit Frequency Filters
Margret Keuper, Julia Grabinski, Janis Keuper
Convolution Goes Higher-Order: A Biologically Inspired Mechanism Empowers Image Classification
Simone Azeglio, Olivier Marre, Peter Neri et al.
Fourier Analysis Network
Yihong Dong, Ge Li, Yongding Tao et al.
From Linear to Nonlinear: Provable Weak-to-Strong Generalization through Feature Learning
Junsoo Oh, Jerry Song, Chulhee Yun
msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML
Zhaolan Huang, Emmanuel Baccelli
Optimal Brain Apoptosis
Mingyuan Sun, Zheng Fang, Jiaxu Wang et al.
Reverse Convolution and Its Applications to Image Restoration
Xuhong Huang, Shiqi Liu, Kai Zhang et al.
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning
Jingyang Li, Jiachun Pan, Vincent Tan et al.
Vision CNNs trained to estimate spatial latents learned similar ventral-stream-aligned representations
Yudi Xie, Weichen Huang, Esther Alter et al.
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy
Kirill Vishniakov, Zhiqiang Shen, Zhuang Liu
FedHARM: Harmonizing Model Architectural Diversity in Federated Learning
Anestis Kastellos, Athanasios Psaltis, Charalampos Z Patrikakis et al.
Gradient-Aware for Class-Imbalanced Semi-supervised Medical Image Segmentation
Wenbo Qi, Jiafei Wu, S. C. Chan
Is Kernel Prediction More Powerful than Gating in Convolutional Neural Networks?
Lorenz K. Muller
Linking in Style: Understanding learned features in deep learning models
Maren Wehrheim, Pamela Osuna Vargas, Matthias Kaschube
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields
Tom Fischer, Pascal Peter, Joachim Weickert et al.
Pick-or-Mix: Dynamic Channel Sampling for ConvNets
Ashish Kumar, Daneul Kim, Jaesik Park et al.
Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective
Yajie Bao, Michael Crawshaw, Mingrui Liu
Quantization-Friendly Winograd Transformations for Convolutional Neural Networks
Vladimir Protsenko, Vladimir Kryzhanovskiy, Alexander Filippov
Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness
Honghao Chen, Zhang Yurong, xiaokun Feng et al.
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio Video Point Cloud Time-Series and Image Recognition
Xiaohan Ding, Yiyuan Zhang, Yixiao Ge et al.
VideoMAC: Video Masked Autoencoders Meet ConvNets
Gensheng Pei, Tao Chen, Xiruo Jiang et al.
Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
Yuxiao Wen, Arthur Jacot