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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size
TLDR
This work proposes a small DNN architecture called SqueezeNet, which achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters and is able to compress to less than 0.5MB (510x smaller than AlexNet). Expand
- LEVEL ACCURACY WITH 50 X FEWER PARAMETERS AND < 0 . 5 MB MODEL SIZE
Recent research on deep convolutional neural networks (CNNs) has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple CNN architecturesExpand
From captions to visual concepts and back
TLDR
This paper uses multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives, and develops a maximum-entropy language model. Expand
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
TLDR
DenseNet is presented, an open source system that computes dense, multiscale features from the convolutional layers of a CNN based object classifier. Expand
Deformable part models are convolutional neural networks
TLDR
This paper shows that a DPM can be formulated as a CNN, thus providing a synthesis of the two ideas and calls the resulting model a DeepPyramid DPM, which is found to significantly outperform DPMs based on histograms of oriented gradients features (HOG) and slightly outperforms a comparable version of the recently introduced R-CNN detection system, while running significantly faster. Expand
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
TLDR
SqueezeDet is a fully convolutional neural network for object detection that aims to simultaneously satisfy all of the above constraints, and is very accurate, achieving state-of-the-art accuracy on the KITTI benchmark. Expand
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters
TLDR
FireCaffe is presented, which successfully scales deep neural network training across a cluster of GPUs, and finds that reduction trees are more efficient and scalable than the traditional parameter server approach. Expand
Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction
TLDR
This paper proposes two pose-normalized descriptors based on computationally-efficient deformable part models based on strongly-supervised DPM parts, which enable pooling across pose and viewpoint, in turn facilitating tasks such as fine-grained recognition and attribute prediction. Expand
How to scale distributed deep learning?
TLDR
It is found, perhaps counterintuitively, that asynchronous SGD, including both elastic averaging and gossiping, converges faster at fewer nodes, whereas synchronous SGD scales better to more nodes (up to about 100 nodes). Expand
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer
TLDR
This work proposes several DCNN architectures, several of which surpass published state-of-art accuracy on a popular logo recognition dataset and applies DCNNs to logo recognition. Expand
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