PPT-Inception and Residual Architecture in Deep Convolutional N

Wenchi Ma Computer Vision Group EECSKU Inception From NIN to Googlenet m icro network A general nonlinear function approximator Enhance the abstraction ability of the local model. RECOGNITION. does size matter?. Karen . Simonyan. Andrew . Zisserman. Contents. Why I Care. Introduction. Convolutional Configuration . Classification. Experiments. Conclusion. Big Picture. Why I . care. 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: . ISHAY BE’ERY. ELAD KNOLL. OUTLINES. . Motivation. Model . c. ompression: mimicking large networks:. FITNETS : HINTS FOR THIN DEEP NETS . (A. Romero, 2014). DO DEEP NETS REALLY NEED TO BE DEEP . (Rich Caruana & Lei Jimmy Ba 2014). Recognition. Author : . Kaiming. He, . Xiangyu. Zhang, . Shaoqing. Ren, and Jian Sun. (accepted to CVPR 2016). Presenter : . Hyeongseok. Son. The deeper, the better. The deeper network can cover more complex problems. 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. Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). The Future of Real-Time Rendering?. 1. Deep Learning is Changing the Way We Do Graphics. [Chaitanya17]. [Dahm17]. [Laine17]. [Holden17]. [Karras17]. [Nalbach17]. Video. “. Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion”. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. 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. 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 . Google. Pierre. Sermanet,. Google. Dumitru. Erhan,. Google. Wei. Liu,. UNC. Yangqing. Jia,. Google. Scott. Reed,. University of Michigan. Dragomir. Anguelov,. Google. Vincent. Vanhoucke,. Google. Andrew. Commercially . available seizure detection systems suffer from unacceptably high false alarm rates. . Deep . learning algorithms, like Convolutional Neural Networks (CNNs), have not previously been effective due to the lack of big data resources. . Subset of the publicly available TUH EEG Corpus (. www.isip.piconepress.com/projects/tuh_eeg). .. Evaluation Data:. 50 patients, 239 sessions, 1015 files. 171 hours of data including 16 hours of seizures.. 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.