Deep Metric Learning via Lifted Structured Feature Embedding

Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works have shown promising results on discriminatively training the networks to learn semantic feature embeddings where similar examples are mapped close to each other and dissimilar examples are mapped farther apart... (read more)

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Datasets


Introduced in the Paper:

Stanford Online Products

Mentioned in the Paper:

CUB-200-2011

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Methods used in the Paper


METHOD TYPE
1x1 Convolution
Convolutions
Convolution
Convolutions
Average Pooling
Pooling Operations
Local Response Normalization
Normalization
Auxiliary Classifier
Miscellaneous Components
Inception Module
Image Model Blocks
ReLU
Activation Functions
Dropout
Regularization
Dense Connections
Feedforward Networks
Max Pooling
Pooling Operations
Softmax
Output Functions
GoogLeNet
Convolutional Neural Networks