Comments (7)
In the paper, we also discussed the data augmentation, and thus RandomAddGaussian() means randomly adding Gaussian noise in each training sample and is one kind of the data augmentation method. Data augmentation is a common step in image classification, which can sometimes enhance the generalization ability of the model. Thus, we discussed it.
from dl-based-intelligent-diagnosis-benchmark.
Hi. The file CNN_Datasets/R_NA/datasets/CWRUFFT.py, at line 105, adds noise to the training dataset, despite being used for a classification task. Is that correct?
In the paper, we also discussed the data augmentation, and thus RandomAddGaussian() means randomly adding Gaussian noise in each training sample and is one kind of the data augmentation method. Data augmentation is a common step in image classification, which can sometimes enhance the generalization ability of the model. Thus, we discussed it.
from dl-based-intelligent-diagnosis-benchmark.
Thanks for your response. That is right.
However, shouldn't datasets on R_NA folder be free of data augmentation?
from dl-based-intelligent-diagnosis-benchmark.
Thanks for your response. That is right. However, shouldn't datasets on R_NA folder be free of data augmentation?
Yes, you are right! We have made a mistake when cleaning up our codes. I have modified this error, which happens in both AE and CNN
from dl-based-intelligent-diagnosis-benchmark.
Nice! Many thanks. This seems to happen to other datasets in CNN_Datasets/R_NA folder too:
- CWRUCWT.py
- JNUCWT.py
- MFPTCWT.py
- PUCWT.py
- PU.py
- SEUCWT.py
- UoCCWT.py
- XJTUCWT.py
They all use RandomScaler and RandomCrop.
from dl-based-intelligent-diagnosis-benchmark.
Nice! Many thanks. This seems to happen to other datasets in CNN_Datasets/R_NA folder too:
- CWRUCWT.py
- JNUCWT.py
- MFPTCWT.py
- PUCWT.py
- PU.py
- SEUCWT.py
- UoCCWT.py
- XJTUCWT.py
They all use RandomScaler and RandomCrop.
Thanks very much!
from dl-based-intelligent-diagnosis-benchmark.
Resolved.
from dl-based-intelligent-diagnosis-benchmark.
Related Issues (20)
- Error
- 运行结果 HOT 3
- 请赵老师指点 HOT 2
- 交流群 HOT 1
- /tmp文件夹有什么用 HOT 2
- PU数据集的label
- MFPT填的数据集地址为啥会报错
- 运行train_ae.py之后数据集改为CWRU,出现一个警告.请问怎么解决
- TypeError: 'float' object is not iterable HOT 4
- [BiLSTM] - View size is not compatible with input tensor's size and stride
- 江南大学轴承数据集链接失效
- 使用cwru_stft
- SEU dataset dead link
- XJTU数据处理
- --processing_type O_A doesn't work
- For test HOT 1
- error HOT 2
- whether model Ae_2d has some wrongly setting parameters ? HOT 1
- 每个数据集的重采样长度只有100,而且每个数据集只用到了1/10的数据? HOT 4
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from dl-based-intelligent-diagnosis-benchmark.