Search Results for author:
Found 9 papers, 5 papers with code
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization Perspective
The reason for the high-performance of large kernel CNNs in downstream tasks has been attributed to the large effective receptive field (ERF) produced by large size kernels, but this view has not been fully tested.
iMixer: hierarchical Hopfield network implies an invertible, implicit and iterative MLP-Mixer
In the last few years, the success of Transformers in computer vision has stimulated the discovery of many alternative models that compete with Transformers, such as the MLP-Mixer.
FFT-based Dynamic Token Mixer for Vision
Their computational complexity is proportional to quadratic numbers of pixels in input feature maps, resulting in slow processing, especially when dealing with high-resolution images.
Example-Based Explainable AI and its Application for Remote Sensing Image Classification
We present a method of explainable artificial intelligence (XAI), "What I Know (WIK)", to provide additional information to verify the reliability of a deep learning model by showing an example of an instance in a training dataset that is similar to the input data to be inferred and demonstrate it in a remote sensing image classification task.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+3
Sequencer: Deep LSTM for Image Classification
Here we propose Sequencer, a novel and competitive architecture alternative to ViT that provides a new perspective on these issues.
Ranked #20 on
Image Classification
on ImageNet V2
Application of QUBO solver using black-box optimization to structural design for resonance avoidance
Quadratic unconstrained binary optimization (QUBO) solvers can be applied to design an optimal structure to avoid resonance.
Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network
One of the promising approaches for explaining such a black-box system is counterfactual explanation.
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?
This leaves open the possibility of incorporating a non-convolutional (or non-local) inductive bias into the architecture, so we used two simple ideas to incorporate inductive bias into the MLP-Mixer while taking advantage of its ability to capture global correlations.
Deep Residual Networks and Weight Initialization
Residual Network (ResNet) is the state-of-the-art architecture that realizes successful training of really deep neural network.
