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Introduction

간단하게 paper 정리.

Papers

Google Inception Models.
Decoupled Neural Interfaces using Synthetic Gradients.
Neural Turing Machine.
Google Neural Machine Translation System.
Swivel: Improving Embeddings by Noticing What's Missing.
Attention-based Extraction of Structured Information from Street View Imagery.
Convolutional Sequence to Sequence Learning
Deep Image Retrival
Learning From Noisy Large-Scale Datasets With Minimal Supervision
Building Mobile Applications with TensorFlow
Deep Metric Learning via Facility Location
Large-Scale Image Retrieval with Attentive Deep Local Features
Why do deep convolutional networks generalize so poorly to small image transformations?
CBAM: Convolutional Block Attention Module
Wide Resnet
DropBlock: A regularization method for convolution networks
Bag of tricks for image classification w/ CNN
Domain adaptive transfer learning w/ specialist model
Do better ImageNet models transfer better?
EfficientNet: Rethinking Model Scaling for CNN.
ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
Billion-scale semi-supervised learning for image classification.
MultiGrain: A unified image embedding for classes and instances.
Hidden Technical Debt in Machine Learning Systems.
Benchmarking neural network robustness to common corruptions and perturbations.
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network.
Learning to Rank Images with Cross-Modal Graph Convolutions.
Are we done with ImageNet?

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