PPT-Similarity Learning with (or without) Convolutional Neural Network

Moitreya Chatterjee Yunan Luo Image Source Google Outline This Section Why do we need Similarity Measures Metric Learning as a measure of Similarity Notion of a metric Unsupervised Metric Learning. RECOGNITION. does size matter?. Karen . Simonyan. Andrew . Zisserman. Contents. Why I Care. Introduction. Convolutional Configuration . Classification. Experiments. Conclusion. Big Picture. Why I . care. Cost function. Machine Learning. Neural Network (Classification). Binary classification. . . 1 output unit. Layer 1. Layer 2. Layer 3. Layer 4. Multi-class classification . (K classes). K output units. Neural . Network Architectures:. f. rom . LeNet. to ResNet. Lana Lazebnik. Figure source: A. . Karpathy. What happened to my field?. . Classification:. . ImageNet. Challenge top-5 error. Figure source: . 2015/10/02. 陳柏任. Outline. Neural Networks. Convolutional Neural Networks. Some famous CNN structure. Applications. Toolkit. Conclusion. Reference. 2. Outline. Neural Networks. Convolutional Neural Networks. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . By, . . Sruthi. . Moola. Convolution. . Convolution is a common image processing technique that changes the intensities of a pixel to reflect the intensities of the surrounding pixels. A common use of convolution is to create image filters. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. Aaron Schumacher. Data Science DC. 2017-11-14. Aaron Schumacher. planspace.org has these slides. Plan. applications. : . what. t. heory. applications. : . how. onward. a. pplications: what. Backgammon. Article and Work by. : Justin . Salamon. and Juan Pablo Bello. Presented by . : . Dhara. Rana. Overall Goal of Paper. Create a way to classify environmental sound given an audio clip. Other methods of sound classification: (1) dictionary learning and (2) wavelet filter banks . Convolutional Codes COS 463 : Wireless Networks Lecture 9 Kyle Jamieson [Parts adapted from H. Balakrishnan ] So far, we’ve seen block codes Convolutional Codes: Simple design, especially at the transmitter Prabhas. . Chongstitvatana. Faculty of Engineering. Chulalongkorn. university. More Information. Search “Prabhas Chongstitvatana”. Go to me homepage. Perceptron. Rosenblatt, 1950. Multi-layer perceptron. n,k. ) code by adding the r parity digits. An alternative scheme that groups the data stream into much smaller blocks k digits and encode them into n digits with order of k say 1, 2 or 3 digits at most is the convolutional codes. Such code structure can be realized using convolutional structure for the data digits.. Kannan . Neten. Dharan. Introduction . Alzheimer’s Disease is a kind of dementia which is caused by damage to nerve cells in the brain and the usual side effects of it are loss of memory or other cognitive impairments.. An overview and applications. Outline. Overview of Convolutional Neural Networks. The Convolution operation. A typical CNN model architecture. Properties of CNN models. Applications of CNN models. Notable CNN models.