Deep Image: Scaling up Image Recognition

2015/01/13 by Ren Wu, Wu Ren, Shengen Yan +8 · 1 voice · 10 citations
Computer Science · #Advanced Neural Network Applications #Advanced Image and Video Retrieval Techniques #Human Pose and Action Recognition

paper · pdf · doi:10.48550/arxiv.1501.02876

Abstract

We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning. The key components are a custom-built supercomputer dedicated to deep learning, a highly optimized parallel algorithm using new strategies for data partitioning and communication, larger deep neural network models, novel data augmentation approaches, and usage of multi-scale high-resolution images. Our method achieves excellent results on multiple challenging computer vision benchmarks.

Citations

• Deep convolutional filter banks for texture recognition and segmentation
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ImageNet classification with deep convolutional neural networks
• Bilinear CNNs for Fine-grained Visual Recognition
• Deep Blue

Cited by

• 8-Bit Approximations for Parallelism in Deep Learning
• Probabilistic Backpropagation for Scalable Learning of Bayesian Neural\n Networks
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations
• Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes
• Stanza: Layer Separation for Distributed Training in Deep Learning
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• Deep Learning with Limited Numerical Precision
• Lets keep it simple, Using simple architectures to outperform deeper and\n more complex architectures
• Social Media Image Analysis for Public Health

Discussions

• Deep Image: Scaling Up Image Recognition [hn, 21 points, 5 comments]

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