전체 글71 [X:AI] SimCLR 논문 리뷰 A Simple Framework for Contrastive Learning of Visual Representations논문 원본 : https://arxiv.org/abs/2002.05709 A Simple Framework for Contrastive Learning of Visual RepresentationsThis paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architecture.. 2024. 7. 14. [X:AI] Grad-CAM 논문 리뷰 Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization 논문 원본 : https://arxiv.org/abs/1610.02391 Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationWe propose a technique for producing "visual explanations" for decisions from a large class of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Ma.. 2024. 7. 6. [Stanford cs231n] Lecture 11(Detection&Segmentation) 강의 주제Object DetectionSemantic SegmentationInstance SegmentationR-CNNFast R-CNNFaster R-CNNMask R-CNN강의 영상https://www.youtube.com/watch?v=nDPWywWRIRo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=11강의 자료https://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf 2024. 6. 16. [Stanford cs231n] Lecture 10(Recurrent Neural Networks) 강의 주제RNN, LSTM강의 영상https://www.youtube.com/watch?v=6niqTuYFZLQ&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=10강의 자료https://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf 2024. 6. 16. [X:AI] Taskonomy 논문 리뷰 Taskonomy: Disentangling Task Transfer Learning 논문 원본 : https://arxiv.org/abs/1804.08328발표 영상 : https://www.youtube.com/watch?v=rKw-vg6jtt8발표 자료 Taskonomy: Disentangling Task Transfer LearningDo visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying exis.. 2024. 5. 21. [X:AI] Mask R-CNN 논문 리뷰 Mask R-CNN논문 원본 https://arxiv.org/abs/1703.06870발표 영상 https://www.youtube.com/watch?v=fZ5ZD68n9GI발표 자료 (오타 ICLR 2017 -> CVPR 2017) Mask R-CNNWe present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, calledar.. 2024. 5. 6. 이전 1 ··· 3 4 5 6 7 8 9 ··· 12 다음
