Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition

ICLR 2019 • Chun-Fu Chen • Quanfu Fan • Neil Mallinar • Tom Sercu • Rogerio Feris

In this paper, we propose a novel Convolutional Neural Network (CNN) architecture for learning multi-scale feature representations with good tradeoffs between speed and accuracy. This is achieved by using a multi-branch network, which has different computational complexity at different branches... (read more)

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METHOD	TYPE
Average Pooling	Pooling Operations
ResNeXt Block	Skip Connection Blocks
Grouped Convolution	Convolutions
ResNeXt	Convolutional Neural Networks
Batch Normalization	Normalization
Bottleneck Residual Block	Skip Connection Blocks
Global Average Pooling	Pooling Operations
Residual Block	Skip Connection Blocks
Linear Layer	Feedforward Networks
Dense Connections	Feedforward Networks
Kaiming Initialization	Initialization
Max Pooling	Pooling Operations
1x1 Convolution	Convolutions
Softmax	Output Functions
Random Horizontal Flip	Image Data Augmentation
Random Resized Crop	Image Data Augmentation
Cosine Annealing	Learning Rate Schedules
Nesterov Accelerated Gradient	Stochastic Optimization
Weight Decay	Regularization
ReLU	Activation Functions
Big-Little Module	Skip Connection Blocks
Big-Little Net	Convolutional Neural Networks
Residual Connection	Skip Connections
Convolution	Convolutions
ResNet	Convolutional Neural Networks

METHOD

TYPE

Average Pooling