Image Classification
Deep Residual Learning for Image Recognition
Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun
Microsoft Research
{kahe, v-xiangz, v-shren, jiansun}@microsoft.com
Abstract
Deeper neural networks are more difficult to train. We
present a residual learning framework to ease the training
of networks that are substantially deeper than those used
previously. We explicitly reformulate the layers as learn-
ing residual functions with reference to the layer inputs, in-
stead of learning unreferenced functions. We provide com-
prehensive empirical evidence showing that these residual
networks are easier to optimize, and can gain accuracy from
considerably increased depth. On the ImageNet dataset we
evaluate residual nets with a depth of up to 152 layers—8⇥
deeper than VGG nets [41] but still having lower complex-
ity. An ensemble of these residual nets achieves 3.57% error
on the ImageNet test set. This result won the 1st place on the
ILSVRC 2015 classification task. We also present analysis
on CIFAR-10 with 100 and 1000 layers.
The depth of representations is of central importance
for many visual recognition tasks. Solely due to our ex-
tremely deep representations, we obtain a 28% relative im-
provement on the COCO object detection dataset. Deep
residual nets are foundations of our submissions to ILSVRC
& COCO 2015 competitions1, where we also won the 1st
places on the tasks of ImageNet detection, ImageNet local-
ization, COCO detection, and COCO segmentation.
1. Introduction
Deep convolutional neural networks [22, 21] have led
to a series of breakthroughs for image classification [21,
50, 40]. Deep networks naturally integrate low/mid/high-
level features [50] and classifiers in an end-to-end multi-
layer fashion, and the “levels” of features can be enriched
by the number of stacked layers (depth). Recent evidence
[41, 44] reveals that network depth is of crucial importance,
and the leading results [41, 44, 13, 16] on the challenging
ImageNet dataset [36] all exploit “very deep” [41] models,
with a depth of sixteen [41] to thirty [16]. Many other non-
trivial visual recognition tasks [8, 12, 7, 32, 27] have also
1http://image-net.org/challenges/LSVRC/2015/ and
http://mscoco.org/dataset/#detections-challenge2015.
0 1 2 3 4 5 6
0
10
20
iter. (1e4)
training error (%)
0 1 2 3 4 5 6
0
10
20
iter. (1e4)
test error (%)
56-layer
20-layer
56-layer
20-layer
Figure 1. Training error (left) and test error (right) on CIFAR-10
with 20-layer and 56-layer “plain” networks. The deeper network
has higher training error, and thus test error. Similar phenomena
on ImageNet is presented in Fig. 4.
greatly benefited from very deep models.
Driven by the significance of depth, a question arises: Is
learning better networks as easy as stacking more layers?
An obstacle to answering this question was the notorious
problem of vanishing/exploding gradients [1, 9], which
hamper convergence from the beginning. This problem,
however, has been largely addressed by normalized initial-
ization [23, 9, 37, 13] and intermediate normalization layers
[16], which enable networks with tens of layers to start con-
verging for stochastic gradient descent (SGD) with back-
propagation [22].
When deeper networks are able to start converging, a
degradation problem has been exposed: with the network
depth increasing, accuracy gets saturated (which might be
unsurprising) and then degrades rapidly. Unexpectedly,
such degradation is not caused by overfitting, and adding
more layers to a suitably deep model leads to higher train-
ing error, as reported in [11, 42] and thoroughly verified by
our experiments. Fig. 1 shows a typical example.
The degradation (of training accuracy) indicates that not
all systems are similarly easy to optimize. Let us consider a
shallower architecture and its deeper counterpart that adds
more layers onto it. There exists a solution by construction
to the deeper model: the added layers are identity mapping,
and the other layers are copied from the learned shallower
model. The existence of this constructed solution indicates
that a deeper model should produce no higher training error
than its shallower counterpart. But experiments show that
our current solvers on hand are unable to find solutions that
1
arXiv:1512.03385v1 [cs.CV] 10 Dec 2015
Densely Connected Convolutional Networks
Gao Huang⇤
Cornell University
[email protected]
Zhuang Liu⇤
Tsinghua University
[email protected]
Laurens van der Maaten
Facebook AI Research
[email protected]
Kilian Q. Weinberger
Cornell University
[email protected]
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 Convo-
lutional 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 com-
pelling advantages: they alleviate the vanishing-gradient
problem, strengthen feature propagation, encourage fea-
ture reuse, and substantially reduce the number of parame-
ters. We evaluate our proposed architecture on four highly
competitive object recognition benchmark tasks (CIFAR-10,
CIFAR-100, SVHN, and ImageNet). DenseNets obtain sig-
nificant improvements over the state-of-the-art on most of
them, whilst requiring less computation to achieve high per-
formance. Code and pre-trained models are available at
https://github.com/liuzhuang13/DenseNet
.
1. Introduction
Convolutional neural networks (CNNs) have become
the dominant machine learning approach for visual object
recognition. Although they were originally introduced over
20 years ago [18], improvements in computer hardware and
network structure have enabled the training of truly deep
CNNs only recently. The original LeNet5 [19] consisted of
5 layers, VGG featured 19 [29], and only last year Highway
⇤Authors contributed equally
x0
x1
H1
x2
H2
H3
H4
x3
x4
Figure 1: A 5-layer dense block with a growth rate of k = 4.
Each layer takes all preceding feature-maps as input.
Networks [34] and Residual Networks (ResNets) [11] have
surpassed the 100-layer barrier.
As CNNs become increasingly deep, a new research
problem emerges: as information about the input or gra-
dient passes through many layers, it can vanish and “wash
out” by the time it reaches the end (or beginning) of the
network. Many recent publications address this or related
problems. ResNets [11] and Highway Networks [34] by-
pass signal from one layer to the next via identity connec-
tions. Stochastic depth [13] shortens ResNets by randomly
dropping layers during training to allow better information
and gradient flow. FractalNets [17] repeatedly combine sev-
eral parallel layer sequences with different number of con-
volutional blocks to obtain a large nominal depth, while
maintaining many short paths in the network. Although
these different approaches vary in network topology and
training procedure, they all share a key characteristic: they
create short paths from early layers to later layers.
1
arXiv:1608.06993v5 [cs.CV] 28 Jan 2018
Inception Recurrent Convolutional Neural Network for Object Recognition
Md Zahangir Alom [email protected]
University of Dayton, Dayton, OH, USA
Mahmudul Hasan [email protected]
Comcast Labs, Washington, DC, USA
Chris Yakopcic [email protected]
University of Dayton, Dayton, OH, USA
Tarek M. Taha [email protected]
University of Dayton, Dayton, OH, USA
Abstract
Deep convolutional neural networks (DCNNs)
are an influential tool for solving various prob-
lems in the machine learning and computer vi-
sion fields. In this paper, we introduce a
new deep learning model called an Inception-
Recurrent Convolutional Neural Network (IR-
CNN), which utilizes the power of an incep-
tion network combined with recurrent layers in
DCNN architecture. We have empirically eval-
uated the recognition performance of the pro-
posed IRCNN model using different benchmark
datasets such as MNIST, CIFAR-10, CIFAR-
100, and SVHN. Experimental results show sim-
ilar or higher recognition accuracy when com-
pared to most of the popular DCNNs including
the RCNN. Furthermore, we have investigated
IRCNN performance against equivalent Incep-
tion Networks and Inception-Residual Networks
using the CIFAR-100 dataset. We report about
3.5%, 3.47% and 2.54% improvement in classifi-
cation accuracy when compared to the RCNN,
equivalent Inception Networks, and Inception-
Residual Networks on the augmented CIFAR-
100 dataset respectively.
1. Introduction
In recent years, deep learning using Convolutional Neu-
ral Networks (CNNs) has shown enormous success in the
field of machine learning and computer vision. CNNs pro-
vide state-of-the-art accuracy in various image recognition
tasks including object recognition (Schmidhuber, 2015;
Krizhevsky et al., 2012; Simonyan & Zisserman, 2014;
Szegedy et al., 2015), object detection (Girshick et al.,
2014), tracking (Wang et al., 2015), and image caption-
ing (Xu et al., 2014). In addition, this technique has been
applied massively in computer vision tasks such as video
representation and classification of human activity (Bal-
las et al., 2015). Machine translation and natural language
processing are applied deep learning techniques that show
great success in this domain (Collobert & Weston, 2008;
Manning et al., 2014). Furthermore, this technique has
been used extensively in the field of speech recognition
(Hinton et al., 2012). Moreover, deep learning is not lim-
ited to signal, natural language, image, and video process-
ing tasks, it has been applying successfully for game devel-
opment (Mnih et al., 2013; Lillicrap et al., 2015). There is
a lot of ongoing research for developing even better perfor-
mance and improving the training process of DCNNs (Lin
et al., 2013; Springenberg et al., 2014; Goodfellow et al.,
2013; Ioffe & Szegedy, 2015; Zeiler & Fergus, 2013).
In some cases, machine intelligence shows better perfor-
mance compared to human intelligence including calcula-
tion, chess, memory, and pattern matching. On the other
hand, human intelligence still provides better performance
in other fields such as object recognition, scene under-
standing, and more. Deep learning techniques (DCNNs
in particular) perform very well in the domains of detec-
tion, classification, and scene understanding. There is a
still a gap that must be closed before human level intelli-
gence is reached when performing visual recognition tasks.
Machine intelligence may open an opportunity to build a
system that can process visual information the way that a
human brain does. According to the study on the visual
processing system within a human brain by James DiCarlo
et al. (Zoccolan & Rust, 2012) the brain consists of sev-
eral visual processing units starting with the visual cortex
arXiv:1704.07709v1 [cs.CV] 25 Apr 2017
2015-2017
Supervised
Im
age
Classification
danilop
ivargrimstad
chobishiba
lhespanha
tkhs0604
subomi
shibe23
vrallev
mot_techtalk
iuchimasatoshi
devoc
hschwentner
sferik
thekraken
davidbonilla
swwweet
bluesmoon
palkan
denniskardys
cassininazir
scottboms
mojombo
zakiwarfel
thoeni