https://github.com/graykode/dac
Deep Adaptive Image Clustering Paper Implementation
https://github.com/graykode/dac
Last synced: about 1 year ago
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Deep Adaptive Image Clustering Paper Implementation
- Host: GitHub
- URL: https://github.com/graykode/dac
- Owner: graykode
- Created: 2018-12-29T08:17:09.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-29T08:21:45.000Z (over 7 years ago)
- Last Synced: 2025-03-30T21:51:10.824Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 178 KB
- Stars: 29
- Watchers: 2
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
Deep Adaptive Image Clustering
---
### Paper Summarize
**DAC(Deep Adaptive Image Clustering) is Unsupervisor Learning that use Adaptive Deep Learning Algorithm**
1. Each Images(Train Set & Test Set) labels of features is generated by ConvNet(7 Convloutions Layer and 2 Fully-Connected Layer)
```python
def ConvNetwork(in_img, num_cluster, name='ConvNetwork', reuse=False):
...
return out
```
2. This Label features is calculated by cosine similarities
```python
label_feat = ConvNetwork(image_pool_input, num_cluster, name='ConvNetwork', reuse=False)
label_feat_norm = tf.nn.l2_normalize(label_feat, dim=1)
sim_mat = tf.matmul(label_feat_norm, label_feat_norm, transpose_b=True)
```
3. Adaptive Algorithm can Optimize Which Images is more similar using this Cost Function
```python
pos_loc = tf.greater(sim_mat, u_thres, name='greater')
neg_loc = tf.less(sim_mat, l_thres, name='less')
pos_loc_mask = tf.cast(pos_loc, dtype=tf.float32)
neg_loc_mask = tf.cast(neg_loc, dtype=tf.float32)
pred_label = tf.argmax(label_feat, axis=1)
# Deep Adaptive Image Clustering Cost Function Optimize
pos_entropy = tf.multiply(-tf.log(tf.clip_by_value(sim_mat, eps, 1.0)), pos_loc_mask)
neg_entropy = tf.multiply(-tf.log(tf.clip_by_value(1-sim_mat, eps, 1.0)), neg_loc_mask)
loss_sum = tf.reduce_mean(pos_entropy) + tf.reduce_mean(neg_entropy)
train_op = tf.train.RMSPropOptimizer(lr).minimize(loss_sum)
```
4. This Algorithm runs Until u > l confidence is right
## Usage
- MNIST : You can run MNIST DAC on [This Google Colab](https://colab.research.google.com/drive/1OdoFwOnVvpR1jhlLweqM90pltyQYjeLm)
#### Common Images
Folder Structure Example
```
+-- train
| 12345678_000001.jpg
| 12345678_000002.jpg
| 1111111_000001.jpg
| 1111111_000002.jpg
+-- test
| 12345678_000003.jpg
| 12345678_000004.jpg
| 1111111_000003.jpg
```
File name `12345678_000001.jpg` The front of `_` means label of image and back is image's ID.
Parameter Setting
```python
mode = 'Training'
num_cluster = 153
eps = 1e-10
height = 300
width = 300
channel = 3
```
## Reference
- [The original ICCV paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Chang_Deep_Adaptive_Image_ICCV_2017_paper.pdf)
- [Deep Adaptive Image Clustering Youtube](https://www.youtube.com/watch?v=03sq7GPHc6E)
- [Author implementation in Keras](https://github.com/vector-1127/DAC)
- [DAC-tensorflow(MNIST)](https://github.com/HongtaoYang/DAC-tensorflow)