Papers › Oriented Response Networks

Oriented Response Networks

7 Jan 2017CVPR 2017 7arXiv:1701.01833archive 2025-07-28

Yanzhao Zhou, Qixiang Ye, Qiang Qiu, Jianbin Jiao

Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations. However, their ability in handling significant local and global image rotations remains limited. In this paper, we propose Active Rotating Filters (ARFs) that actively rotate during convolution and produce feature maps with location and orientation explicitly encoded. An ARF acts as a virtual filter bank containing the filter itself and its multiple unmaterialised rotated versions. During back-propagation, an ARF is collectively updated using errors from all its rotated versions. DCNNs using ARFs, referred to as Oriented Response Networks (ORNs), can produce within-class rotation-invariant deep features while maintaining inter-class discrimination for classification tasks. The oriented response produced by ORNs can also be used for image and object orientation estimation tasks. Over multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we consistently observe that replacing regular filters with the proposed ARFs leads to significant reduction in the number of network parameters and improvement in classification performance. We report the best results on several commonly used benchmarks.

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Code

ZhouYanzhao/ORN officialmentioned on GitHubtorchBSD-3-Clause report

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Tasks

General Classification Image Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Classification	CIFAR-10	ORN	Percentage correct	97.02	#94 of 265	Archive leaderboard	report
Image Classification	CIFAR-100	ORN	Percentage correct	83.85	#83 of 211	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution DCNN Dense Connections Dropout Global Average Pooling Kaiming Initialization Max Pooling ReLU Residual Block Residual Connection Softmax

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