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  "author": "Yunyang Xiong",
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  "author": "Yunyang Xiong",
            
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    {
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    {
            
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      "extra_author": "Hyunwoo J. Kim",
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      "extra_author": "Hyunwoo J. Kim",
            
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    },
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    {
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      "extra_author": "Varsha Hedau",
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      "extra_author": "Varsha Hedau",
            
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  "name": 
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  "name": 
            
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volutional-neural-networks-for-resource-ef-cient-image-classi-cation",
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volutional-neural-networks-for-resource-ef-cient-image-classi-cation",
            
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  "notes": "Deep convolutional neural networks have achieved 
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  "notes": "Deep convolutional neural networks have achieved 
            
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remarkable success in computer vision. However, deep neural networks 
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remarkable success in computer vision. However, deep neural networks 
            
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require large computing resources to achieve high performance. 
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require large computing resources to achieve high performance. 
            
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Although depthwise separable convolution can be an efficient module to 
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Although depthwise separable convolution can be an efficient module to 
            
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approximate a standard convolution, it often leads to reduced 
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approximate a standard convolution, it often leads to reduced 
            
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representational power of networks. In this paper, under budget 
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representational power of networks. In this paper, under budget 
            
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constraints such as computational cost (MAdds) and the parameter 
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constraints such as computational cost (MAdds) and the parameter 
            
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count, we propose a novel basic architectural block, ANTBlock. It 
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count, we propose a novel basic architectural block, ANTBlock. It 
            
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boosts the representational power by modeling, in a high dimensional 
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boosts the representational power by modeling, in a high dimensional 
            
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space, interdependency of channels between a depthwise convolution 
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space, interdependency of channels between a depthwise convolution 
            
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layer and a projection layer in the ANTBlocks. Our experiments show 
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layer and a projection layer in the ANTBlocks. Our experiments show 
            
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that ANTNet built by a sequence of ANTBlocks, consistently outperforms 
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that ANTNet built by a sequence of ANTBlocks, consistently outperforms 
            
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state-of-the-art low-cost mobile convolutional neural networks across 
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state-of-the-art low-cost mobile convolutional neural networks across 
            
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multiple datasets. On CIFAR100, our model achieves 75.7% top-1 
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multiple datasets. On CIFAR100, our model achieves 75.7% top-1 
            
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accuracy, which is 1.5% higher than MobileNetV2 with 8.3% fewer 
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accuracy, which is 1.5% higher than MobileNetV2 with 8.3% fewer 
            
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parameters and 19.6% less computational cost. On ImageNet, our model 
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parameters and 19.6% less computational cost. On ImageNet, our model 
            
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achieves 72.8% top-1 accuracy, which is 0.8% improvement, with 157.7ms 
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achieves 72.8% top-1 accuracy, which is 0.8% improvement, with 157.7ms 
            
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(20% faster) on iPhone 5s over MobileNetV2.",
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(20% faster) on iPhone 5s over MobileNetV2.",
            
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    {
            
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      "display_name": "Depthwise Separable Convolution",
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      "display_name": "Depthwise Separable Convolution",
            
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      "id": "b1bdb59d-7cfb-4d4f-82bd-0bd7012f64f3",
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      "id": "b1bdb59d-7cfb-4d4f-82bd-0bd7012f64f3",
            
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      "name": "Depthwise Separable Convolution",
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      "name": "Depthwise Separable Convolution",
            
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      "state": "active",
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      "state": "active",
            
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      "display_name": "ImageNet",
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      "display_name": "ImageNet",
            
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      "id": "114653a3-d688-42fb-8e76-350752af988b",
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      "id": "114653a3-d688-42fb-8e76-350752af988b",
            
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      "name": "ImageNet",
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      "name": "ImageNet",
            
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      "state": "active",
            
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      "vocabulary_id": null
            
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    {
            
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      "display_name": "MobileNetV2",
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      "display_name": "MobileNetV2",
            
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      "id": "1f906d45-2f51-4822-9daf-eb6d6194e53c",
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      "id": "1f906d45-2f51-4822-9daf-eb6d6194e53c",
            
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "ANTNets: Mobile Convolutional Neural Networks for Resource 
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  "title": "ANTNets: Mobile Convolutional Neural Networks for Resource 
            
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Ef\ufb01cient Image Classi\ufb01cation",
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Ef\ufb01cient Image Classi\ufb01cation",
            
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  "type": "dataset",
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  "version": ""
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  "version": ""
            
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}
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