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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Journal ArticleDOI
Speech synthesis from ECoG using densely connected 3D convolutional neural networks
Miguel Angrick,Christian Herff,Christian Herff,Emily M. Mugler,Matthew C. Tate,Marc W. Slutzky,Dean J. Krusienski,Tanja Schultz +7 more
TL;DR: This is the first time that high-quality speech has been reconstructed from neural recordings during speech production using deep neural networks, and uses a densely connected convolutional neural network topology which is well-suited to work with the small amount of data available from each participant.
Proceedings ArticleDOI
SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images
TL;DR: A large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated, and proposes an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties.
Proceedings ArticleDOI
Task2Vec: Task Embedding for Meta-Learning
Alessandro Achille,Michael Lam,Rahul Tewari,Avinash Ravichandran,Subhransu Maji,Charless C. Fowlkes,Stefano Soatto,Pietro Perona +7 more
TL;DR: A method to generate vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations, and is demonstrated to be capable of predicting task similarities that match the authors' intuition about semantic and taxonomic relations between different visual tasks.
Journal ArticleDOI
Impact of a deep learning assistant on the histopathologic classification of liver cancer.
Amirhossein Kiani,Bora Uyumazturk,Pranav Rajpurkar,Alex Wang,Rebecca W. Gao,Erik Jones,Yifan Yu,Curtis P. Langlotz,Robyn L. Ball,Thomas J. Montine,Brock A. Martin,Gerald J. Berry,Michael G. Ozawa,Florette K. Hazard,Ryanne A. Brown,Simon B. Chen,Mona Wood,Libby S. Allard,Lourdes Ylagan,Andrew Y. Ng,Jeanne Shen +20 more
TL;DR: A deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and its effect on the diagnostic performance of 11 pathologists with varying levels of expertise is evaluated.
Posted Content
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning
Chi-Hung Hsu,Shih-Chieh Chang,Jhao-Hong Liang,Hsin-Ping Chou,Chun-Hao Liu,Shu-Huan Chang,Tim Pan,Yu-Ting Chen,Wei Wei,Da-Cheng Juan +9 more
TL;DR: Experimental results showed that, compared to the state-ofthe-arts, models found by MONAS achieve comparable or better classification accuracy on computer vision applications, while satisfying the additional objectives such as peak power.
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