일 | 월 | 화 | 수 | 목 | 금 | 토 |
---|---|---|---|---|---|---|
1 | ||||||
2 | 3 | 4 | 5 | 6 | 7 | 8 |
9 | 10 | 11 | 12 | 13 | 14 | 15 |
16 | 17 | 18 | 19 | 20 | 21 | 22 |
23 | 24 | 25 | 26 | 27 | 28 | 29 |
30 | 31 |
Tags
- 메타러닝
- 기계학습
- coding test
- 머신러닝
- 코딩 테스트
- meta-learning
- 설명가능한 인공지능
- Class activation map
- 인공지능
- Deep learning
- keras
- Cam
- Machine Learning
- Artificial Intelligence
- GAN
- grad-cam
- xai
- python
- AI
- 시계열 분석
- Score-CAM
- 딥러닝
- Unsupervised learning
- Interpretability
- Explainable AI
- 설명가능한
- 백준
- 코딩테스트
- cs231n
- SmoothGrad
Archives
- Today
- Total
목록edge detector (1)
iMTE

논문 제목 : Sanity checks for saliency maps 논문 주소 : arxiv.org/abs/1810.03292 Sanity Checks for Saliency Maps Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an a arxiv.org 주요 내용 : 1) Saliency map은 학..
Deep learning study/Explainable AI, 설명가능한 AI
2021. 4. 20. 15:12