Inception-v3 Module Explained

Inception-v3 Module

Introduced by Szegedy et al. in Rethinking the Inception Architecture for Computer Vision

Inception-v3 Module is an image block used in the Inception-v3 architecture. This architecture is used on the coarsest (8 × 8) grids to promote high dimensional representations.

Source: Rethinking the Inception Architecture for Computer Vision

Paper Code Snippet

Latest Papers

PAPER	DATE
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TanhSoft -- a family of activation functions combining Tanh and Softplus Koushik Biswas • Sandeep Kumar • Shilpak Banerjee • Ashish Kumar Pandey	2020-09-08
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Causal Explanations of Image Misclassifications Yan Min • Miles Bennett	2020-06-28
A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan \| Abdolkarim Saeedi • Maryam Saeedi • Arash Maghsoudi	2020-06-24
BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach Albara Ah Ramli • Rex Liu • Rahul Krishnamoorthy • Vishal I B • Xiaoxiao Wang • Ilias Tagkopoulos • Xin Liu	2020-06-01
Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI Anas Tahir • Yazan Qiblawey • Amith Khandakar • Tawsifur Rahman • Uzair Khurshid • Farayi Musharavati • M. T. Islam • Serkan Kiranyaz • Muhammad E. H. Chowdhury	2020-05-23
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices Chunjie Luo • Xiwen He • Jianfeng Zhan • Lei Wang • Wanling Gao • Jiahui Dai	2020-05-07
3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning from sEMG Sensors David Lonsdale • Li Zhang • Richard Jiang	2020-05-04
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks Narinder Singh Punn • Sonali Agarwal	2020-04-23
GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm Dalia Ezzat • Aboul ell Hassanien • Hassan Aboul Ella	2020-04-09
A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants Denis P. Rubanga • Loyani K. Loyani • Mgaya Richard • Sawahiko Shimada	2020-04-08
Identifying Individual Dogs in Social Media Images Djordje Batic • Dubravko Culibrk	2020-03-14
Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages Muhammad Ali Farooq • Asma Khatoon • Viktor Varkarakis • Peter Corcoran	2020-03-13
SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images Soumyajyoti Dey • Soham Das • Swarnendu Ghosh • Shyamali Mitra • Sukanta Chakrabarty • Nibaran Das	2020-03-12
Neuron Shapley: Discovering the Responsible Neurons Amirata Ghorbani • James Zou	2020-02-23
Introducing Fuzzy Layers for Deep Learning Stanton R. Price • Steven R. Price • Derek T. Anderson	2020-02-21
Exponential discretization of weights of neural network connections in pre-trained neural networks Magomed Yu. Malsagov • Emil M. Khayrov • Maria M. Pushkareva • Iakov M. Karandashev	2020-02-03
ZeroQ: A Novel Zero Shot Quantization Framework \| Yaohui Cai • Zhewei Yao • Zhen Dong • Amir Gholami • Michael W. Mahoney • Kurt Keutzer	2020-01-01
Linear Mode Connectivity and the Lottery Ticket Hypothesis \| Jonathan Frankle • Gintare Karolina Dziugaite • Daniel M. Roy • Michael Carbin	2019-12-11
Scratch that! An Evolution-based Adversarial Attack against Neural Networks Malhar Jere • Loris Rossi • Briland Hitaj • Gabriela Ciocarlie • Giacomo Boracchi • Farinaz Koushanfar	2019-12-05
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Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss Qian Wang • Toby P. Breckon	2019-11-18
Predictive modeling of brain tumor: A Deep learning approach Priyansh Saxena • Akshat Maheshwari • Shivani Tayal • Saumil Maheshwari	2019-11-06
Establishing an Evaluation Metric to Quantify Climate Change Image Realism Sharon Zhou • Alexandra Luccioni • Gautier Cosne • Michael S. Bernstein • Yoshua Bengio	2019-10-22
GDP: Generalized Device Placement for Dataflow Graphs Yanqi Zhou • Sudip Roy • Amirali Abdolrashidi • Daniel Wong • Peter C. Ma • Qiumin Xu • Ming Zhong • Hanxiao Liu • Anna Goldie • Azalia Mirhoseini • James Laudon	2019-09-28
Histographs: Graphs in Histopathology Shrey Gadiya • Deepak Anand • Amit Sethi	2019-08-14
Genetic Deep Learning for Lung Cancer Screening Hunter Park • Connor Monahan	2019-07-27
Mimic and Fool: A Task Agnostic Adversarial Attack Akshay Chaturvedi • Utpal Garain	2019-06-11
Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset \| Misgina Tsighe Hagos • Shri Kant	2019-05-17
Double Transfer Learning for Breast Cancer Histopathologic Image Classification Jonathan de Matos • Alceu de S. Britto Jr. • Luiz E. S. Oliveira • Alessandro L. Koerich	2019-04-16
Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification Chollette C. Olisah • Lyndon Smith	2019-03-27
DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras \| Oleksandr Bogdan • Viktor Eckstein • Francois Rameau • Jean-Charles Bazin	2018-12-15
Accelerating Convolutional Neural Networks via Activation Map Compression Georgios Georgiadis	2018-12-10
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects \| Michael A. Alcorn • Qi Li • Zhitao Gong • Chengfei Wang • Long Mai • Wei-Shinn Ku • Anh Nguyen	2018-11-28
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning \| Alex Olsen • Dmitry A. Konovalov • Bronson Philippa • Peter Ridd • Jake C. Wood • Jamie Johns • Wesley Banks • Benjamin Girgenti • Owen Kenny • James Whinney • Brendan Calvert • Mostafa Rahimi Azghadi • Ronald D. White	2018-10-09
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference Jeffrey L. McKinstry • Steven K. Esser • Rathinakumar Appuswamy • Deepika Bablani • John V. Arthur • Izzet B. Yildiz • Dharmendra S. Modha	2018-09-11
DFT-based Transformation Invariant Pooling Layer for Visual Classification Jongbin Ryu • Ming-Hsuan Yang • Jongwoo Lim	2018-09-01
Dank Learning: Generating Memes Using Deep Neural Networks \| Abel L Peirson V • E Meltem Tolunay	2018-06-08
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization \| Moustafa Alzantot • Yash Sharma • Supriyo Chakraborty • Huan Zhang • Cho-Jui Hsieh • Mani Srivastava	2018-05-28
ADef: an Iterative Algorithm to Construct Adversarial Deformations \| Rima Alaifari • Giovanni S. Alberti • Tandri Gauksson	2018-04-20
Value-aware Quantization for Training and Inference of Neural Networks Eunhyeok Park • Sungjoo Yoo • Peter Vajda	2018-04-20
Human Semantic Parsing for Person Re-identification Mahdi M. Kalayeh • Emrah Basaran • Muhittin Gokmen • Mustafa E. Kamasak • Mubarak Shah # 26 PERSON RE-IDENTIFICATION ON MARKET-1501	2018-03-31
Classification of breast cancer histology images using transfer learning Sulaiman Vesal • Nishant Ravikumar • AmirAbbas Davari • Stephan Ellmann • Andreas Maier	2018-02-26
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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning \| Christian Szegedy • Sergey Ioffe • Vincent Vanhoucke • Alex Alemi # 60 IMAGE CLASSIFICATION ON IMAGENET	2016-02-23
RandomOut: Using a convolutional gradient norm to rescue convolutional filters Joseph Paul Cohen • Henry Z. Lo • Wei Ding	2016-02-18
Rethinking the Inception Architecture for Computer Vision \| Christian Szegedy • Vincent Vanhoucke • Sergey Ioffe • Jonathon Shlens • Zbigniew Wojna # 79 IMAGE CLASSIFICATION ON IMAGENET	2015-12-02

Tasks

TASK	PAPERS	SHARE
Image Classification	10	22.73%
Quantization	4	9.09%
Adversarial Attack	3	6.82%
Object Detection	2	4.55%
Semantic Segmentation	2	4.55%
Image Captioning	2	4.55%
Skin Lesion Classification	2	4.55%
Computed Tomography (CT)	1	2.27%
Skin Cancer Classification	1	2.27%