A Hybrid Supervised-unsupervised Method on Image Topic Visualization with Convolutional Neural Network and LDA

15 Mar 2017 Kai Zhen Mridul Birla David Crandall Bingjing Zhang Judy Qiu

Given the progress in image recognition with recent data driven paradigms, it's still expensive to manually label a large training data to fit a convolutional neural network (CNN) model. This paper proposes a hybrid supervised-unsupervised method combining a pre-trained AlexNet with Latent Dirichlet Allocation (LDA) to extract image topics from both an unlabeled life-logging dataset and the COCO dataset... (read more)

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Methods used in the Paper


METHOD TYPE
LDA
Dimensionality Reduction
1x1 Convolution
Convolutions
Convolution
Convolutions
Local Response Normalization
Normalization
Grouped Convolution
Convolutions
ReLU
Activation Functions
Dropout
Regularization
Dense Connections
Feedforward Networks
Max Pooling
Pooling Operations
AlexNet
Convolutional Neural Networks
Softmax
Output Functions