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## Description
Implement MNIST dataset using Convolutional Neural Networks (CNNs). Use Max Pooling Layers, Conv2d layers, Dense layers.
Use your own strategy and try to achieve the maximum possible …
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Traceback (most recent call last):
File "/root/miniconda3/envs/oldMotor/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call
return fn(*args)
File "/root…
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**Is your feature request related to a problem? Please describe.**
Data can be aggregated via visual screenshots of current map preview.
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CNNs can be seen as nonlinear operators.
Two ideas here:
1. use a CNNs as operator where only the forward is implemented; CNN can be used as regularizers ([link](https://github.com/google/RED))
…
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With baseline CNNs with no anti-aliasing, we see better shift consistency if we increase the CNN's depth, e.g. VGG11 -> VGG19, Resnet18 -> Resnet152. Why is that so?
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I want to know if you have the code of the paper Depth-Adapted CNNs for RGB-D Semantic Segmentation? Thank you
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## Feature request
Request the implementation of the following ONNX operators:
* LogSoftmax
* Softmax
* ReduceMax
## Motivation
These operators are common in neural networks of many types;…
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Spherical approaches to convolutions solve problems such as 360 video or global geographical data. A spherical implementation can be found at https://openreview.net/pdf?id=Hkbd5xZRb.
Because convol…
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## 書誌情報
### 論文リンク
https://arxiv.org/pdf/2010.02178.pdf
### 著者/所属機関
### conf/journal
### year
2020
## どんな論文か?
- Demonstrating how the padding mechanism can cause spatial artifacts in CNNs
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## 一言でいうと
セグメンテーションのタスクで、色やテクスチャーではなく形状に注目する機構を導入した研究。具体的には、通常の画像を処理するネットワーク(Regular Stream)と並行してAttentionからShape検知を行うShape Streamを導入する。Shapeの学習を行うため、Boundaryに関するlossを組み込んでいる。
![image](https://u…