"convolutional neural networks" Papers

Convolution Goes Higher-Order: A Biologically Inspired Mechanism Empowers Image Classification

Simone Azeglio, Olivier Marre, Peter Neri et al.

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.

A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks

Boqi Li, Weiwei Liu

Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN

Minsoo Kang, Minkoo Kang, Suhyun Kim

ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy

Kirill Vishniakov, Zhiqiang Shen, Zhuang Liu

Entropy Induced Pruning Framework for Convolutional Neural Networks

Yiheng Lu, Ziyu Guan, Yaming Yang et al.

How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model

Umberto Tomasini, Matthieu Wyart

Is Kernel Prediction More Powerful than Gating in Convolutional Neural Networks?

Lorenz K. Muller

Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning

Tom Nuno Wolf, Fabian Bongratz, Anne-Marie Rickmann et al.

Make RepVGG Greater Again: A Quantization-Aware Approach

Xuesong Nie, Yunfeng Yan, Siyuan Li et al.

Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks

Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields

Tom Fischer, Pascal Peter, Joachim Weickert et al.

Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective

Yajie Bao, Michael Crawshaw, Mingrui Liu

ResDiff: Combining CNN and Diffusion Model for Image Super-resolution

Shuyao Shang, Zhengyang Shan, Guangxing Liu et al.

Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness

Honghao Chen, Zhang Yurong, xiaokun Feng et al.

Sample-specific Masks for Visual Reprogramming-based Prompting

Chengyi Cai, Zesheng Ye, Lei Feng et al.

Targeted Activation Penalties Help CNNs Ignore Spurious Signals

Dekai Zhang, Matt Williams, Francesca Toni

Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning

Yuxiao Wen, Arthur Jacot