DAC
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Deep Adaptive Image Clustering Paper Implementation
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Deep Adaptive Image Clustering
Paper Summarize
DAC(Deep Adaptive Image Clustering) is Unsupervisor Learning that use Adaptive Deep Learning Algorithm
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Each Images(Train Set & Test Set) labels of features is generated by ConvNet(7 Convloutions Layer and 2 Fully-Connected Layer)
def ConvNetwork(in_img, num_cluster, name='ConvNetwork', reuse=False): ... return out -
This Label features is calculated by cosine similarities
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) -
Adaptive Algorithm can Optimize Which Images is more similar using this Cost Function
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) -
This Algorithm runs Until u > l confidence is right
Usage
- MNIST : You can run MNIST DAC on This Google Colab
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
mode = 'Training'
num_cluster = 153
eps = 1e-10
height = 300
width = 300
channel = 3
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