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README.md
꼼꼼한 딥러닝 논문 리뷰와 코드 실습: Deep Learning Paper Review and Practice
- 꼼꼼한 딥러닝 논문 리뷰와 코드 실습을 위한 저장소입니다.
- 최신 논문 위주로, 많은 인기를 끌고 있는 다양한 딥러닝 논문을 소개합니다.
- 질문 사항은 본 저장소의 이슈(Issues) 탭에 남겨주세요.
Image Recognition (이미지 인식)
- End-to-End Object Detection with Transformers (ECCV 2020)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- Searching for MobileNetV3 (ICCV 2019)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- Deep Residual Learning for Image Recognition (CVPR 2016)
- Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization (ICCV 2017)
- Image Style Transfer Using Convolutional Neural Networks (CVPR 2016)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (NIPS 2015)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
Natural Language Processing (자연어 처리)
- Single Headed Attention RNN: Stop Thinking With Your Head (2020)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (NAACL 2019)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- Attention is All You Need (NIPS 2017)
- Neural Machine Translation by Jointly Learning to Align and Translate (ICLR 2015 Oral)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- Show and Tell: A Neural Image Caption Generator (CVPR 2015)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- Sequence to Sequence Learning with Neural Networks (NIPS 2014)
Generative Model & Super Resolution (생성 모델 & 해상도 복원)
- Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR 2020)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- SinGAN: Learning a Generative Model from a Single Natural Image (ICCV 2019)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- A Style-Based Generator Architecture for Generative Adversarial Networks (CVPR 2019)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation (CVPR 2018 Oral)
- Image-to-Image Translation with Conditional Adversarial Networks (CVPR 2017)
- Generative Adversarial Nets (NIPS 2014)
Modeling & Optimization (모델링 & 최적화)
- Bag of Tricks for Image Classification (CVPR 2019)
- Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding (ICLR 2016 Oral)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
- Batch normalization: Accelerating deep network training by reducing internal covariate shift (PMLR 2015)
Adversarial Examples & Backdoor Attacks (적대적 예제 & 백도어 공격)
- HopSkipJumpAttack: A Query-Efficient Decision-Based Attack (S&P 2020)
- Original Paper Link / Paper Review Video/ Summary PDF / Targeted Attack / Untargeted Attack
- Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates (ICLR 2020)
- Sign-OPT: A Query-Efficient Hard-label Adversarial Attack (ICLR 2020)
- Original Paper Link / Paper Review Video / Summary PDF / MNIST / CIFAR-10
- Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment (AAAI 2020 Oral)
- Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach (ICLR 2019)
- Original Paper Link / Paper Review Video / Summary PDF / MNIST / CIFAR-10
- Boosting Adversarial Attacks with Momentum (CVPR 2018 Spotlight)
- Original Paper Link / Paper Review Video / Summary PDF / CIFAR-10 / ImageNet
- Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks (NIPS 2018)
- Original Paper Link / Paper Review Video / Summary PDF / ResNet / AlexNet
- Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models (ICLR 2018)
- Original Paper Link / Paper Review Video / Summary PDF / Code Practice
지난 논문 리뷰 콘텐츠
- Explaining and Harnessing Adversarial Examples (ICLR 2015)
- Towards Evaluating the Robustness of Neural Networks (S&P 2017)
- Towards Deep Learning Models Resistant to Adversarial Attacks (ICLR 2018)
- Adversarial Examples Are Not Bugs, They Are Features (NIPS 2019)
- Certified Robustness to Adversarial Examples with Differential Privacy (S&P 2019)
- Obfuscated Gradients Give a False Sense of Security (ICML 2018)
- Constructing Unrestricted Adversarial Examples with Generative Models (NIPS 2018)
- Adversarial Patch (NIPS 2018)