Densely Connected Convolutional Networks
Gao Huang,Zhuang Liu,Laurens van der Maaten,Kilian Q. Weinberger +3 more
- pp 2261-2269
TLDR
DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.Abstract:
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections—one between each layer and its subsequent layer—our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.read more
Citations
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Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss.
TL;DR: Two deep convolutional neural networks are applied to automatically measure the knee OA severity, as assessed by the Kellgren-Lawrence (KL) grading system, and both knee joint detection and knee KL grading achieve state-of-the-art performance.
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Learning Attentive Pairwise Interaction for Fine-Grained Classification
Peiqin Zhuang,Yali Wang,Yu Qiao +2 more
TL;DR: Wang et al. as discussed by the authors proposed an attentive pairwise interaction network (API-Net) which can progressively recognize a pair of fine-grained images by interaction, which learns a mutual feature vector to capture semantic differences in the input pair and compares this mutual vector with individual vectors to generate gates for each input image.
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A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
TL;DR: This survey summarizes the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection, lesions and organ segmentation, and systematically categorizes different kinds of medical domainknowledge that have been utilized and their corresponding integrating methods.
Proceedings ArticleDOI
Learning Filter Basis for Convolutional Neural Network Compression
TL;DR: This paper tries to reduce the number of parameters of CNNs by learning a basis of the filters in convolutional layers, and validate the proposed solution for multiple CNN architectures on image classification and image super-resolution benchmarks.
Book ChapterDOI
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases
Pierre Stock,Moustapha Cisse +1 more
TL;DR: It is experimentally demonstrated that the accuracy and robustness of ConvNets measured on Imagenet are vastly underestimated and that explanations can mitigate the impact of misclassified adversarial examples from the perspective of the end-user.
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