Seminar 2020
![](https://www.google.com/images/icons/product/drive-32.png)
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Deformable DETR: Deformable Transformers for End-to-End Object Detection
![](https://www.google.com/images/icons/product/drive-32.png)
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
![](https://www.google.com/images/icons/product/drive-32.png)
Distilling the Knowledge in a Neural Network
Distilling the Knowledge in a Neural Network
![](https://www.google.com/images/icons/product/drive-32.png)
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
![](https://www.google.com/images/icons/product/drive-32.png)
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
![](https://www.google.com/images/icons/product/drive-32.png)
GRU: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
GRU: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
![](https://www.google.com/images/icons/product/drive-32.png)
Attention Based CNN-ConvLSTM for Pedestrian Attribute Recognition
Attention Based CNN-ConvLSTM for Pedestrian Attribute Recognition
![](https://www.google.com/images/icons/product/drive-32.png)
A Closer Look at Spatiotemporal Convolutions for Action Recognition
A Closer Look at Spatiotemporal Convolutions for Action Recognition
![](https://www.google.com/images/icons/product/drive-32.png)
The Electronic Nose Technology in Clinical Diagnosis: A systematic review
The Electronic Nose Technology in Clinical Diagnosis: A systematic review
![](https://www.google.com/images/icons/product/drive-32.png)
Mask R-CNN
Mask R-CNN
![](https://www.google.com/images/icons/product/drive-32.png)
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
Recurrent Nets that Time and Count
Recurrent Nets that Time and Count
![](https://www.google.com/images/icons/product/drive-32.png)
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
![](https://www.google.com/images/icons/product/drive-32.png)
Bootstrap your own latent: A new approach to self-supervised Learning
Bootstrap your own latent: A new approach to self-supervised Learning
Efficient Neural Architecture Search via Parameter Sharing
Efficient Neural Architecture Search via Parameter Sharing
![](https://www.google.com/images/icons/product/drive-32.png)
M-NET: A Convolutional Neural Network For Deep Brain Structure Segmentation
M-NET: A Convolutional Neural Network For Deep Brain Structure Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
![](https://www.google.com/images/icons/product/drive-32.png)
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
![](https://www.google.com/images/icons/product/drive-32.png)
Synthesizer: Rethinking Self-Attention in Transformer Models with Natural Language Processing
Synthesizer: Rethinking Self-Attention in Transformer Models with Natural Language Processing
![](https://www.google.com/images/icons/product/drive-32.png)
Attention Is All You Need
Attention Is All You Need
![](https://www.google.com/images/icons/product/drive-32.png)
Pruning Filters for Efficient ConvNets
![](https://www.google.com/images/icons/product/drive-32.png)
ImageNet Classification with Deep Convolutional Neural Networks
![](https://www.google.com/images/icons/product/drive-32.png)
Designing Network Design Spaces
Designing Network Design Spaces
![](https://www.google.com/images/icons/product/drive-32.png)
Dynamic Routing Between Capsules
![](https://www.google.com/images/icons/product/drive-32.png)
Fixing the train-test resolution discrepancy
![](https://www.google.com/images/icons/product/drive-32.png)
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
![](https://www.google.com/images/icons/product/drive-32.png)
Dynamic Curriculum Learning for Imbalanced Data Classification
Dynamic Curriculum Learning for Imbalanced Data Classification
![](https://www.google.com/images/icons/product/drive-32.png)
CBAM : Convolutional Block Attention Module
CBAM : Convolutional Block Attention Module
![](https://www.google.com/images/icons/product/drive-32.png)
EfficientDet: Scalable and Efficient Object Detection
EfficientDet: Scalable and Efficient Object Detection