Inception Module Explained | Papers With Code

Inception Module

Introduced by Szegedy et al. in Going Deeper with Convolutions

An Inception Module is an image model block that aims to approximate an optimal local sparse structure in a CNN. Put simply, it allows for us to use multiple types of filter size, instead of being restricted to a single filter size, in a single image block, which we then concatenate and pass onto the next layer.

Source: Going Deeper with Convolutions

Paper Code Snippet

Latest Papers

PAPER	DATE
From Sound Representation to Model Robustness Mohammad Esmaeilpour • Patrick Cardinal • Alessandro Lameiras Koerich	2020-07-27
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes Marcelo Gennari do Nascimento • Theo W. Costain • Victor Adrian Prisacariu	2020-07-15
Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes? \| Ella M. Gale • Nicholas Martin • Ryan Blything • Anh Nguyen • Jeffrey S. Bowers	2020-07-02
Causal Explanations of Image Misclassifications Yan Min • Miles Bennett	2020-06-28
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs Aditya Rajagopal • Diederik Adriaan Vink • Stylianos I. Venieris • Christos-Savvas Bouganis	2020-06-16
Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning Jie An • Tao Li • Haozhi Huang • Li Shen • Xuan Wang • Yongyi Tang • Jinwen Ma • Wei Liu • Jiebo Luo	2020-06-16
Intriguing generalization and simplicity of adversarially trained neural networks Chirag Agarwal • Peijie Chen • Anh Nguyen	2020-06-16
Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs Behnam Pourghassemi • Chenghao Zhang • Joo Hwan Lee • Aparna Chandramowlishwaran	2020-05-28
Synthesizing Unrestricted False Positive Adversarial Objects Using Generative Models Martin Kotuliak • Sandro E. Schoenborn • Andrei Dan	2020-05-19
On the safety of vulnerable road users by cyclist orientation detection using Deep Learning Marichelo Garcia-Venegas • Diego A. Mercado-Ravell • Carlos A. Carballo-Monsivais	2020-04-25
SIGN: Scalable Inception Graph Neural Networks Fabrizio Frasca • Emanuele Rossi • Davide Eynard • Ben Chamberlain • Michael Bronstein • Federico Monti # 2 NODE CLASSIFICATION ON AMZ COMP	2020-04-23
Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review Muhammad Ilyas • Hina Rehman • Amine Nait-ali	2020-04-11
Skin Diseases Detection using LBP and WLD- An Ensembling Approach Arnab Banerjee • Nibaran Das • Mita Nasipuri	2020-04-08
Within the Lack of COVID-19 Benchmark Dataset: A Novel GAN with Deep Transfer Learning for Corona-virus Detection in Chest X-ray Images Mohamed Loey • Florentin Smarandache • Nour Eldeen M. Khalifa	2020-04-07
Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset Nour Eldeen M. Khalifa • Mohamed Hamed N. Taha • Aboul Ella Hassanien • Sally Elghamrawy	2020-04-02
Classification of the Chinese Handwritten Numbers with Supervised Projective Dictionary Pair Learning Rasool Ameri • Saideh Ferdowsi • Ali Alameer • Vahid Abolghasemi • Kianoush Nazarpour	2020-03-26
Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition Moazam Soomro • Muhammad Ali Farooq • Rana Hammad Raza	2020-03-15
Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls Mengjiao Hu • Kang Sim • Juan Helen Zhou • Xudong Jiang • Cuntai Guan	2020-03-14
Identifying Individual Dogs in Social Media Images Djordje Batic • Dubravko Culibrk	2020-03-14
Traffic Signs Detection and Recognition System using Deep Learning Pavly Salah Zaki • Marco Magdy William • Bolis Karam Soliman • Kerolos Gamal Alexsan • Keroles Khalil • Magdy El-Moursy	2020-03-06
Introducing Fuzzy Layers for Deep Learning Stanton R. Price • Steven R. Price • Derek T. Anderson	2020-02-21
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets Dongxian Wu • Yisen Wang • Shu-Tao Xia • James Bailey • Xingjun Ma	2020-02-14
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks \| Mingyuan Meng • Xingyu Yang • Shanlin Xiao • Zhiyi Yu	2020-01-29
Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction Shruti Jaiswal • Gora Chand Nandi	2020-01-12
RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks Lukas Cavigelli • Luca Benini	2020-01-04
UW-NET: AN INCEPTION-ATTENTION NETWORK FOR UNDERWATER IMAGE CLASSIFICATION Miao Yang and Ke Hu • Chongyi Li • Zhiqiang Wei	2020-01-01
Multi-Precision Policy Enforced Training (MuPPET) : A precision-switching strategy for quantised fixed-point training of CNNs Anonymous	2020-01-01
Random Partition Relaxation for Training Binary and Ternary Weight Neural Network Anonymous	2020-01-01
Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes? Anonymous	2020-01-01
TopoAct: Visually Exploring the Shape of Activations in Deep Learning Archit Rathore • Nithin Chalapathi • Sourabh Palande • Bei Wang	2019-12-13
High-parallelism Inception-like Spiking Neural Networks for Unsupervised Feature Learning \| Mingyuan Meng • Xingyu Yang • Lei Bi • Jinman Kim • Shanlin Xiao • Zhiyi Yu	2019-12-02
AI-based Pilgrim Detection using Convolutional Neural Networks Marwa Ben Jabra • Adel Ammar • Anis Koubaa • Omar Cheikhrouhou • Habib Hamam	2019-11-18
A Scalable Multilabel Classification to Deploy Deep Learning Architectures For Edge Devices Tolulope A. Odetola • Ogheneuriri Oderhohwo • Syed Rafay Hasan	2019-11-05
Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving Der-Hau Lee • Kuan-Lin Chen • Kuan-Han Liou • Chang-Lun Liu • Jinn-Liang Liu	2019-10-26
DSConv: Efficient Convolution Operator Marcelo Gennari do Nascimento • Roger Fawcett • Victor Adrian Prisacariu	2019-10-01
Deep Convolutions for In-Depth Automated Rock Typing E. E. Baraboshkin • L. S. Ismailova • D. M. Orlov • E. A. Zhukovskaya • G. A. Kalmykov • O. V. Khotylev • E. Yu. Baraboshkin • D. A. Koroteev	2019-09-23
Inception-inspired LSTM for Next-frame Video Prediction \| Matin Hosseini • Anthony S. Maida • Majid Hosseini • Gottumukkala Raju	2019-08-28
Fine-Tuning Models Comparisons on Garbage Classification for Recyclability Umut Ozkaya • Levent Seyfi	2019-08-07
Genetic Deep Learning for Lung Cancer Screening Hunter Park • Connor Monahan	2019-07-27
DeepShift: Towards Multiplication-Less Neural Networks \| Mostafa Elhoushi • Zihao Chen • Farhan Shafiq • Ye Henry Tian • Joey Yiwei Li	2019-05-30
C3AE: Exploring the Limits of Compact Model for Age Estimation \| Chao Zhang • Shuaicheng Liu • Xun Xu • Ce Zhu # 1 AGE ESTIMATION ON FGNET	2019-04-10
Unsupervised Feature Learning for Environmental Sound Classification Using Weighted Cycle-Consistent Generative Adversarial Network Mohammad Esmaeilpour • Patrick Cardinal • Alessandro Lameiras Koerich	2019-04-08
A Dilated Inception Network for Visual Saliency Prediction \| Sheng Yang • Guosheng Lin • Qiuping Jiang • Weisi Lin	2019-04-07
End-to-End Video Captioning Silvio Olivastri • Gurkirt Singh • Fabio Cuzzolin	2019-04-04
Evaluating CNNs on the Gestalt Principle of Closure Gregor Ehrensperger • Sebastian Stabinger • Antonio Rodríguez Sánchez	2019-03-30
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI Endre Grøvik • Darvin Yi • Michael Iv • Elisabeth Tong • Daniel L. Rubin • Greg Zaharchuk	2019-03-18
Image Privacy Prediction Using Deep Neural Networks Ashwini Tonge • Cornelia Caragea	2019-03-08
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression Jie Zhang • Xiaolong Wang • Dawei Li • Shalini Ghosh • Abhishek Kolagunda • Yalin Wang	2019-02-03
TUNet: Incorporating segmentation maps to improve classification Yijun Tian	2019-01-27
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning \| Shaohui Lin • Rongrong Ji • Yuchao Li • Cheng Deng • Xuelong Li	2019-01-23
DSConv: Efficient Convolution Operator Marcelo Gennari • Roger Fawcett • Victor Adrian Prisacariu	2019-01-07
Artificial Color Constancy via GoogLeNet with Angular Loss Function \| Oleksii Sidorov	2018-11-20
Structure Learning of Deep Neural Networks with Q-Learning Guoqiang Zhong • Wencong Jiao • Wei Gao	2018-10-31
Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks Haoming Lin • Yuyang Hu • Siping Chen • Jianhua Yao • Ling Zhang	2018-10-14
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification \| S H Shabbeer Basha • Soumen Ghosh • Kancharagunta Kishan Babu • Shiv Ram Dubey • Viswanath Pulabaigari • Snehasis Mukherjee	2018-09-30
Rethinking Numerical Representations for Deep Neural Networks Parker Hill • Babak Zamirai • Shengshuo Lu • Yu-Wei Chao • Michael Laurenzano • Mehrzad Samadi • Marios Papaefthymiou • Scott Mahlke • Thomas Wenisch • Jia Deng • Lingjia Tang • Jason Mars	2018-08-07
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks Yuzhe Ma • Ran Chen • Wei Li • Fanhua Shang • Wenjian Yu • Minsik Cho • Bei Yu	2018-07-26
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks \| Dongqing Zhang • Jiaolong Yang • Dongqiangzi Ye • Gang Hua	2018-07-26
DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration Mohamed S. Abdelfattah • David Han • Andrew Bitar • Roberto DiCecco • Shane OConnell • Nitika Shanker • Joseph Chu • Ian Prins • Joshua Fender • Andrew C. Ling • Gordon R. Chiu	2018-07-13
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions \| Junru Wu • Yue Wang • Zhenyu Wu • Zhangyang Wang • Ashok Veeraraghavan • Yingyan Lin	2018-07-01
Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions \| Junru Wu • Yue Wang • Zhenyu Wu • Zhangyang Wang • Ashok Veeraraghavan • Yingyan Lin	2018-06-24
Age and Gender Classification From Ear Images Dogucan Yaman • Fevziye Irem Eyiokur • Nurdan Sezgin • Hazım Kemal Ekenel	2018-06-14
State Classification with CNN Astha Sharma	2018-06-05
Categorizing Concepts With Basic Level for Vision-to-Language Hanzhang Wang • Hanli Wang • Kaisheng Xu	2018-06-01
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization Frederick Tung • Greg Mori	2018-06-01
Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs Chuanhao Zhuge • Xinheng Liu • Xiaofan Zhang • Sudeep Gummadi • Jinjun Xiong • Deming Chen	2018-03-23
GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent Jeff Daily • Abhinav Vishnu • Charles Siegel • Thomas Warfel • Vinay Amatya	2018-03-15
Data Augmentation by Pairing Samples for Images Classification Hiroshi Inoue	2018-01-09
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ Zhe Zhu • Michael Harowicz • Jun Zhang • Ashirbani Saha • Lars J. Grimm • E. Shelley Hwang • Maciej A. Mazurowski	2017-11-28
Feature Selective Networks for Object Detection Yao Zhai • Jingjing Fu • Yan Lu • Houqiang Li	2017-11-24
Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico Boris Babenko • Jonathan Hersh • David Newhouse • Anusha Ramakrishnan • Tom Swartz	2017-11-16
Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs Shaohuai Shi • Xiaowen Chu	2017-11-16
Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks Vishal Desai • Adam E. Flanders • Paras Lakhani	2017-10-10
Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations \| Klemen Grm • Vitomir Štruc • Anais Artiges • Matthieu Caron • Hazim Kemal Ekenel	2017-10-04
Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design Zhourui Song • Zhenyu Liu • Dongsheng Wang	2017-09-22
Exploring Food Detection using CNNs Eduardo Aguilar • Marc Bolaños • Petia Radeva	2017-09-14
Single Shot Text Detector with Regional Attention \| Pan He • Weilin Huang • Tong He • Qile Zhu • Yu Qiao • Xiaolin Li # 4 SCENE TEXT DETECTION ON COCO-TEXT	2017-09-01
Flower Categorization using Deep Convolutional Neural Networks Ayesha Gurnani • Viraj Mavani • Vandit Gajjar • Yash Khandhediya	2017-08-12
Snowflake: A Model Agnostic Accelerator for Deep Convolutional Neural Networks Vinayak Gokhale • Aliasger Zaidy • Andre Xian Ming Chang • Eugenio Culurciello	2017-08-08
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module \| Wuzhen Shi • Feng Jiang • Debin Zhao # 29 IMAGE SUPER-RESOLUTION ON SET5 - 4X UPSCALING	2017-07-22
Xception: Deep Learning With Depthwise Separable Convolutions \| Francois Chollet	2017-07-01
Weighted-Entropy-Based Quantization for Deep Neural Networks Eunhyeok Park • Junwhan Ahn • Sungjoo Yoo	2017-07-01
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks Shuchang Zhou • Yuzhi Wang • He Wen • Qinyao He • Yuheng Zou	2017-06-22
SEP-Nets: Small and Effective Pattern Networks Zhe Li • Xiaoyu Wang • Xutao Lv • Tianbao Yang	2017-06-13
RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data \| Haifeng Li • Xin Dou • Chao Tao • Zhixiang Hou • Jie Chen • Jian Peng • Min Deng • Ling Zhao	2017-05-30
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning \| Wei Wen • Cong Xu • Feng Yan • Chunpeng Wu • Yandan Wang • Yiran Chen • Hai Li	2017-05-22
UC Merced Submission to the ActivityNet Challenge 2016 Yi Zhu • Shawn Newsam • Zaikun Xu	2017-04-11
Coordinating Filters for Faster Deep Neural Networks \| Wei Wen • Cong Xu • Chunpeng Wu • Yandan Wang • Yiran Chen • Hai Li	2017-03-28
Multilevel Context Representation for Improving Object Recognition Andreas Kölsch • Muhammad Zeshan Afzal • Marcus Liwicki	2017-03-19
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation Chunpeng Wu • Wei Wen • Tariq Afzal • Yongmei Zhang • Yiran Chen • Hai Li	2017-03-12
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations Liangzhen Lai • Naveen Suda • Vikas Chandra	2017-03-08
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work Nizar Massouh • Francesca Babiloni • Tatiana Tommasi • Jay Young • Nick Hawes • Barbara Caputo	2017-02-28
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights \| Aojun Zhou • Anbang Yao • Yiwen Guo • Lin Xu • Yurong Chen	2017-02-10
Deep Learning with Low Precision by Half-wave Gaussian Quantization \| Zhaowei Cai • Xiaodong He • Jian Sun • Nuno Vasconcelos	2017-02-03
Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization Kai Yu • Biao Leng • Zhang Zhang • Dangwei Li • Kaiqi Huang	2016-11-17
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning Tien-Ju Yang • Yu-Hsin Chen • Vivienne Sze	2016-11-16
Gradients of Counterfactuals Mukund Sundararajan • Ankur Taly • Qiqi Yan	2016-11-08
Boosting Image Captioning with Attributes Ting Yao • Yingwei Pan • Yehao Li • Zhaofan Qiu • Tao Mei	2016-11-05
Xception: Deep Learning with Depthwise Separable Convolutions \| François Chollet # 75 IMAGE CLASSIFICATION ON IMAGENET	2016-10-07
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation \| Xin Jin • Le Wu • Xiaodong Li • Xiaokun Zhang • Jingying Chi • Siwei Peng • Shiming Ge • Geng Zhao • Shuying Li	2016-10-07
25 years of CNNs: Can we compare to human abstraction capabilities? Sebastian Stabinger • Antonio Rodríguez-Sánchez • Justus Piater	2016-07-28
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks Xiaojie Jin • Yunpeng Chen • Jian Dong • Jiashi Feng • Shuicheng Yan	2016-07-19
DSD: Dense-Sparse-Dense Training for Deep Neural Networks Song Han • Jeff Pool • Sharan Narang • Huizi Mao • Enhao Gong • Shijian Tang • Erich Elsen • Peter Vajda • Manohar Paluri • John Tran • Bryan Catanzaro • William J. Dally	2016-07-15
NIST: An Image Classification Network to Image Semantic Retrieval Le Dong • Xiuyuan Chen • Mengdie Mao • Qianni Zhang	2016-07-02
Maximizing CNN Accelerator Efficiency Through Resource Partitioning Yongming Shen • Michael Ferdman • Peter Milder	2016-06-30
Going Deeper for Multilingual Visual Sentiment Detection Brendan Jou • Shih-Fu Chang	2016-05-30
Theano-MPI: a Theano-based Distributed Training Framework \| He Ma • Fei Mao • Graham W. Taylor	2016-05-26
Adversarial Diversity and Hard Positive Generation Andras Rozsa • Ethan M. Rudd • Terrance E. Boult	2016-05-05
Refining Architectures of Deep Convolutional Neural Networks Sukrit Shankar • Duncan Robertson • Yani Ioannou • Antonio Criminisi • Roberto Cipolla	2016-04-22
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos \| Kai Kang • Hongsheng Li • Junjie Yan • Xingyu Zeng • Bin Yang • Tong Xiao • Cong Zhang • Zhe Wang • Ruohui Wang • Xiaogang Wang • Wanli Ouyang	2016-04-09
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design \| Minsoo Rhu • Natalia Gimelshein • Jason Clemons • Arslan Zulfiqar • Stephen W. Keckler	2016-02-25
Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation Guo-Sen Xie • Xu-Yao Zhang • Shuicheng Yan • Cheng-Lin Liu	2016-01-29
Factors in Finetuning Deep Model for object detection Wanli Ouyang • Xiaogang Wang • Cong Zhang • Xiaokang Yang	2016-01-20
Deep Learning with S-shaped Rectified Linear Activation Units Xiaojie Jin • Chunyan Xu • Jiashi Feng • Yunchao Wei • Junjun Xiong • Shuicheng Yan	2015-12-22
Deep Neural Decision Forests Peter Kontschieder • Madalina Fiterau • Antonio Criminisi • Samuel Rota Bulo	2015-12-01
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications \| Yong-Deok Kim • Eunhyeok Park • Sungjoo Yoo • Taelim Choi • Lu Yang • Dongjun Shin	2015-11-20
All you need is a good init \| Dmytro Mishkin • Jiri Matas # 12 IMAGE CLASSIFICATION ON MNIST	2015-11-19
Convolutional neural networks with low-rank regularization \| Cheng Tai • Tong Xiao • Yi Zhang • Xiaogang Wang • Weinan E	2015-11-19
Deep Metric Learning via Lifted Structured Feature Embedding \| Hyun Oh Song • Yu Xiang • Stefanie Jegelka • Silvio Savarese	2015-11-19
Traffic Sign Classification Using Deep Inception Based Convolutional Networks \| Mrinal Haloi	2015-11-10
Land Use Classification in Remote Sensing Images by Convolutional Neural Networks Marco Castelluccio • Giovanni Poggi • Carlo Sansone • Luisa Verdoliva	2015-08-01
A Visual Embedding for the Unsupervised Extraction of Abstract Semantics D. Garcia-Gasulla • J. Béjar • U. Cortés • E. Ayguadé • J. Labarta • T. Suzumura • R. Chen	2015-07-31
Cyclical Learning Rates for Training Neural Networks \| Leslie N. Smith	2015-06-03
Object Detection by Labeling Superpixels Junjie Yan • Yinan Yu • Xiangyu Zhu • Zhen Lei • Stan Z. Li	2015-06-01
High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps \| Zhuoyao Zhong • Lianwen Jin • Zecheng Xie	2015-05-19
Exploiting Local Features from Deep Networks for Image Retrieval Joe Yue-Hei Ng • Fan Yang • Larry S. Davis	2015-04-20
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift \| Sergey Ioffe • Christian Szegedy # 128 IMAGE CLASSIFICATION ON IMAGENET	2015-02-11
Attention for Fine-Grained Categorization Pierre Sermanet • Andrea Frome • Esteban Real	2014-12-22
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection Wanli Ouyang • Xiaogang Wang • Xingyu Zeng • Shi Qiu • Ping Luo • Yonglong Tian • Hongsheng Li • Shuo Yang • Zhe Wang • Chen-Change Loy • Xiaoou Tang	2014-12-17
Going Deeper with Convolutions \| Christian Szegedy • Wei Liu • Yangqing Jia • Pierre Sermanet • Scott Reed • Dragomir Anguelov • Dumitru Erhan • Vincent Vanhoucke • Andrew Rabinovich # 141 IMAGE CLASSIFICATION ON IMAGENET	2014-09-17

Tasks

TASK	PAPERS	SHARE
Image Classification	27	19.57%
Object Detection	15	10.87%
Quantization	12	8.70%
Object Recognition	8	5.80%
Domain Adaptation	5	3.62%
Object Classification	4	2.90%
Model Compression	3	2.17%
Semantic Segmentation	2	1.45%
Age Estimation	2	1.45%