Deformable DETR: Deformable Transformers for End-to-End Object Detection

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Distilling the Knowledge in a Neural Network

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

GRU: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Attention Based CNN-ConvLSTM for Pedestrian Attribute Recognition

A Closer Look at Spatiotemporal Convolutions for Action Recognition

The Electronic Nose Technology in Clinical Diagnosis: A systematic review

Mask R-CNN

U-Net: Convolutional Networks for Biomedical Image Segmentation

Recurrent Nets that Time and Count

FaceNet: A Unified Embedding for Face Recognition and Clustering

Bootstrap your own latent: A new approach to self-supervised Learning

Efficient Neural Architecture Search via Parameter Sharing

M-NET: A Convolutional Neural Network For Deep Brain Structure Segmentation

Deep Residual Learning for Image Recognition

Understanding deep learning requires rethinking generalization

Synthesizer: Rethinking Self-Attention in Transformer Models with Natural Language Processing

Attention Is All You Need

Pruning Filters for Efficient ConvNets

ImageNet Classification with Deep Convolutional Neural Networks

Designing Network Design Spaces

Dynamic Routing Between Capsules

Fixing the train-test resolution discrepancy

Deep Residual Learning for Image Recognition

Dynamic Curriculum Learning for Imbalanced Data Classification

CBAM : Convolutional Block Attention Module

EfficientDet: Scalable and Efficient Object Detection