Skip to search formSkip to main contentSkip to account menu

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size

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).
View PDF on arXiv (opens in a new tab)

From captions to visual concepts and back

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.
View on IEEE (opens in a new tab)

DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

DenseNet is presented, an open source system that computes dense, multiscale features from the convolutional layers of a CNN based object classifier.
View PDF on arXiv (opens in a new tab)

- LEVEL ACCURACY WITH 50 X FEWER PARAMETERS AND < 0 . 5 MB MODEL SIZE

A small CNN architecture called SqueezeNet is proposed, which achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters and is able to compress to less than 0.5MB (510× smaller than AlexNet).

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

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.
View on IEEE (opens in a new tab)

Deformable part models are convolutional neural networks

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.
View on IEEE (opens in a new tab)

FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters

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.
View on IEEE (opens in a new tab)

Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction

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.
View on IEEE (opens in a new tab)

How to scale distributed deep learning?

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).
View PDF on arXiv (opens in a new tab)

DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer

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.
View PDF on arXiv (opens in a new tab)
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)