Balanced Binary Neural Networks with Gated Residual

26 Sep 2019 Mingzhu Shen Xianglong Liu Ruihao Gong Kai Han

Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the accuracy of networks still remains a critical issue... (read more)

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Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Classification ImageNet BBG (ResNet-34) Top 1 Accuracy 62.6% # 192
Top 5 Accuracy 84.1% # 115
Image Classification ImageNet BBG (ResNet-18) Top 1 Accuracy 59.4% # 194
Top 5 Accuracy 81.3% # 117

Methods used in the Paper


METHOD TYPE
Dropout
Regularization
Dense Connections
Feedforward Networks
Softmax
Output Functions
VGG
Convolutional Neural Networks
Average Pooling
Pooling Operations
Non Maximum Suppression
Proposal Filtering
SSD
Object Detection Models
ReLU
Activation Functions
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Bottleneck Residual Block
Skip Connection Blocks
Global Average Pooling
Pooling Operations
Residual Block
Skip Connection Blocks
Kaiming Initialization
Initialization
Max Pooling
Pooling Operations
Residual Connection
Skip Connections
Convolution
Convolutions
ResNet
Convolutional Neural Networks