/DAC-1

Deep Adaptive Image Clustering Paper Implementation

Primary LanguageJupyter Notebook

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)

    def ConvNetwork(in_img, num_cluster, name='ConvNetwork', reuse=False):
    ...
	return out

  2. 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)

  3. 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)

  4. This Algorithm runs Until u > l confidence is right

Usage

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

Reference