Inception-ResNet-v2 is a convolutional neural architecture that builds on the Inception family of architectures but incorporates residual connections (replacing the filter concatenation stage of the Inception architecture).

Source: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Latest Papers

PAPER DATE
Attention-Driven Body Pose Encoding for Human Activity Recognition
B DebnathM O'brienS. KumarA Behera
2020-09-29
An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers
| Bernhard LieblManuel Burghardt
2020-04-15
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
Dongxian WuYisen WangShu-Tao XiaJames BaileyXingjun Ma
2020-02-14
2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy
| Lars SchmarjeClaudius ZelenkaUlf GeisenClaus-C. GlüerReinhard Koch
2019-07-30
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations
Wonhee Lee Joonil Na Gunhee Kim
2019-06-01
End-to-End Video Captioning
Silvio OlivastriGurkirt SinghFabio Cuzzolin
2019-04-04
Identifying disease-free chest X-ray images with deep transfer learning
Ken C. L. WongMehdi MoradiJoy WuTanveer Syeda-Mahmood
2019-04-02
Deep neural network ensemble by data augmentation and bagging for skin lesion classification
Manik GoyalJagath C. Rajapakse
2018-07-15
Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2
| Federico BaldassarreDiego González MorínLucas Rodés-Guirao
2017-12-09
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
| Xingcheng ZhangZhizhong LiChen Change LoyDahua Lin
2016-11-17
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
| Christian SzegedySergey IoffeVincent VanhouckeAlex Alemi
2016-02-23

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