https://github.com/bethgelab/cifar10_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Towards Deep Learning Models Resistant to Adversarial Attacks
How does Disagreement Help Generalization against Label Corruption?
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
A Simple Framework for Contrastive Learning of Visual Representations
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Momentum Contrast for Unsupervised Visual Representation Learning
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling
Active Learning for Convolutional Neural Networks: A Core-Set Approach
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.