- [LeNet(1998)] [paper]
- [LeNet-5 (2010)] [paper]
- [AlexNet (2012)] [paper]
- [ZFNet(2013)] [paper]
- [VGGNet (2014)] [paper]
- [GoogleNet/Inception(2014)] [paper]
- [FCN(2014)] [paper]
- [RCNN(2014)] [paper]
- [Deeply-supervised networks(2014)] [paper]
- [ResNet(2015)] [paper]
- [Ladder network(2015)] [paper]
- [YOLO(2015)] [Paper]
- [FractalNet (2016)] [paper]
- [PolyNet/Inception-Residual(2016)] [paper]
- [DenseNet(2016)] [paper] [code]
- [SegNet(2016)] [paper]
- [fast region based CNN(2016)] [paper]
- [Look up based CNN(2016)] [paper]
- [Deep network with stochastic depth(2016)] [paper]
- [ResNeXt(2016)] [paper]
- [SqueezeNet(2016)] [paper] [code]
- [CapsNet(2017)] [paper]
- [MobileNets(2017)] [paper]
- [Xception(2017)] [paper]
- [IRCNN(2017)][paper]
- [ViP CNN(2017)] [paper]
- [Squeeze-and-Excitation Networks(2017)][Paper] [code]
- [MobileFaceNets(2018)] [paper]
- [DCNet and DCNet++(2018)] [paper]
- Object Recognition / Object Classification [SOTA]
- Object Detection [SOTA]
- Semantic Segmentation [SOTA]
- Object Tracking [SOTA]
- [Activity/Action Recognition] [SOTA]
- [Face Recognition]
- [Pose estimation]
- [Video & Image Captioning]
- [Biomedical Imaging] SOTA
- [Remote Sensing]
- [Video Analysis]
- [3D Vision]
- [CNNs for NLP]
- [CNNs for Speech Processing]
- [Adversarial Attacks on CNN] SOTA
- [R-CNN(2013)] [paper]
- [Overfeat(2014)] [paper]
- [Multibox(2014)] [paper]
- [SPPNet(2014)] [paper]
- [MR-CNN(2015)] [paper]
- [Deepbox(2015)] [paper]
- [AttentionNet(2015)] [paper]
- [Fast R-CNN(2015)] [paper]
- [DeepProposal(2015)] [paper]
- [RPN(2015)] [[paper]]
- [Faster R-CNN(2015)] [paper]
- [YOLOv1(2016)] [paper]
- [GCNN(2016)] [paper]
- [AZNet(2016)] [paper]
- [ION(2016)] [paper]
- [HyperNet(2016)] [paper]
- [OHEM(2016)] [paper]
- [CRAFT(2016)] [paper]
- [MultipathNet(2016)] [paper]
- [SSD(2016)] [paper]
- [GBDNet(2016)] [paper]
- [CPF(2016)] [[paper]]
- [MS-CNN(2016)] [paper]
- [R-FCN(2016)] [paper]
- [PVANET(2016)] [paper]
- [DeepIDNet(2016)] [paper]
- [NoC(2016)] [paper]
- [DSSD(2017)] [paper]
- [TDM(2017)] [paper]
- [Feature Pyramid Net(2017)] [paper]
- [YOLOv2(2017)] [paper]
- [RON(2017)] [paper]
- [DCN(2017)] [[paper]]
- [DeNet(2017)] [paper]
- [CoupleNet(2017)] [paper]
- [RetinaNet(2017)] [paper]
- [Mask R-CNN(2017)] [paper]
- [DSOD(2017)] [paper]
- [SMN(2017)] [paper]
- [YOLOv3(2018)] [paper]
- [SIN(2018)] [paper]
- [STDN(2018)] [paper]
- [RefineDet(2018)] [paper]
- [RFBNet(2018)] [paper]
- [Light-Head R-CNN(2017)] [paper]
- [Cascade R-CNN(2017)] [paper]
- [YOLT(2018)] [paper]
- [FSSD(2018)] [paper]
- [ESSD] [paper]
- [MDSSD(2018)] [paper]
- [Pelee(2018)] [paper]
- [Fire SSD(2018)] [paper]
- [MegNet(2018)] [paper]
- [DetNet(2018)] [paper]
- [SSOD(2018)] [paper]
- [CornerNet(2018)] [paper]
- [3D Object Detection(2018)] [paper]
- [ZSD（Zero-Shot Object Detection）(2018)] [paper]
- [OSD（One-Shot object Detection）(2018)] [paper]
- [Weakly Supervised Object Detection(2018)] [paper]
- [Softer-NMS (2018)] [paper]
- [VideoCapsuleNet(2018)] [paper]
- [YOLO3D(2018)] [paper]
- U-Net [arxiv][Pytorch]
- SegNet [arxiv][Caffe]
- DeepLab [arxiv][Caffe]
- FCN [arxiv][tensorflow]
- ENet [arxiv][Caffe]
- LinkNet [arxiv][Torch]
- DenseNet [arxiv[]
- Tiramisu [arxiv]
- DilatedNet [arxiv]
- PixelNet [arxiv][Caffe]
- ICNet [arxiv][Caffe]
- ERFNet [arxiv][Torch]
- RefineNet [arxiv][tensorflow]
- PSPNet [arxiv,pspnet][Caffe]
- Dilated convolution [arxiv][Caffe]
- DeconvNet [arxiv][Caffe]
- FRRN [arxiv][Lasagne]
- GCN [arxiv][PyTorch]
- LRR [arxiv][Matconvnet]
- DUC, HDC [arxiv][PyTorch]
- MultiNet [arxiv] [tensorflow1tensorflow2]
- Segaware [arxiv][Caffe]
- Semantic Segmentation using Adversarial Networks [arxiv] [Chainer]
- In-Place Activated BatchNorm:obtain #1 positions [arxiv] [Pytorch]
- [SegCaps(2018)] [arxiv]
- [SegFinNet(2018)] [arxiv]
- [SUNets(2018)] [arxiv]
- Computer Vision SOTA
- neural-network-papers
- ref-implementations
- ConvNet
- image-video-classification
- object-detection
- object-tracking-and-recognition
- semantic-segmentation
- image-generation
- human-pose-estimation
- low-level-vision
- vision-and-nlp
- other
- face-recognition
- cv-datasets
- First paper on Memorization in DNNs: https://arxiv.org/abs/1611.03530
- A closer look at memorization in Deep Networks: https://arxiv.org/abs/1706.05394
- Opening the Black Box of Deep Neural Networks via Information: https://arxiv.org/abs/1703.00810
- Understanding Deep Convolutional Networks
- A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas
- Survey on the attention based RNN model and its applications in computer vision
- Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
- Best Practices for Applying Deep Learning to Novel Applications
- Adversarial Examples: Attacks and Defenses for Deep Learning
- Adversarial Examples that Fool both Human and Computer Vision
- Deep Learning: A Critical Appraisal
- A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
- Multi-objective Architecture Search for CNNs
- Deep Learning for Generic Object Detection: A Survey
- What I learned from competing against a ConvNet on ImageNet
- neural-network-architectures
- Real-time Object Detection with YOLO, YOLOv2 and now YOLOv3
- Recognition, Detection, Segmentation and Tracking
- Computer Vision: Algorithms and Applications
- Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
- Adversarial Attacks and Defences: A Survey
- Secure Deep Learning Engineering: A Software Quality Assurance Perspective
- Face Recognition: From Traditional to Deep Learning Methods
- Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference
- Deep Learning For Computer Vision Tasks: A review
- Mini-batch Serialization: CNN Training with Inter-layer Data Reuse
Maintainer
Gopala KR / @gopala-kr