Lecture 13: Generative Models
Generative models Explicit density Implicit density Direct Tractable density Approximate density Markov Chain Variational Markov Chain Variational Autoencoder Boltzmann Machine GSN GAN Figure copyright and adapted from Ian Goodfellow, Tutorial on Generative Adversarial Networks, 2017. Today: discuss 3 most popular types of generative models today
Download Lecture 13: Generative Models
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Advertisement
Documents from same domain
NaveenAppiah SagarVare - Stanford University
cs231n.stanford.eduNaveenAppiah Mechanical Engineering nappiahb@stanford.edu SagarVare Stanford ICME svare@stanford.edu ... the popular mobile game - Flappy Bird. It involves navi-gating a bird through a bunch of obstacles. Though, this ... the game emulator and learns to make good decisions over time. It is this simple learning framework and their
Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 2 ...
cs231n.stanford.eduFei-Fei Li & Justin Johnson & Serena Yeung Lecture 2 - April 6, 2017 Administrative: Piazza For questions about midterm, poster session, projects,
Lecture 9: CNN Architectures
cs231n.stanford.eduLecture 9 - 22 May 2, 2017 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners First CNN-based winner. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 23 May 2, 2017 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners ZFNet: Improved hyperparameters over AlexNet. Fei-Fei Li & Justin Johnson & Serena Yeung ...
2017, Challenges, Scale, Visual, Recognition, Ilsvrc, Scale visual recognition challenge
Attention and Transformers Lecture 11
cs231n.stanford.edugraph with shared weights h 0 f W h 1 f W h 2 f W h 3 x 3 y T ... Extract spatial features from a pretrained CNN Image Captioning using spatial features 11 CNN Features: H x W x D h 0 [START] Xu et al, “Show, Attend and Tell: Neural Image Caption Generation with Visual Attention”, ICML 2015 z 0,0 z 0,1 z 0,2 z 1,0 z 1,1 z 1,2 z 2,0 z 2,1 z ...
Transformers, Attention, Graph, Spatial, Attention and transformers
CNNs for Face Detection and Recognition
cs231n.stanford.edudevelopment of object classification, localization and detec-tion techniques. 2.1. Sliding Window In the early development of face detection, researchers tended to treat it as a repetitive task of object classifica-tion, by imposing sliding windows and performing object classification with the neural networks on the window re-gion.
Technique, Faces, Recognition, Object, Detection, For face detection and recognition
Vector, Matrix, and Tensor Derivatives
cs231n.stanford.eduErik Learned-Miller The purpose of this document is to help you learn to take derivatives of vectors, matrices, and higher order tensors (arrays with three dimensions or more), and to help you take ... At this point, we have reduced the original matrix equation (Equation 1) …
Lecture 14: Reinforcement Learning
cs231n.stanford.eduFei-Fei Li & Justin Johnson & Serena Yeung Lecture 14 - May 23, 2017 Markov Decision Process 19 - Mathematical formulation of the RL problem - Markov property: Current state completely characterises the state of the
Convolutional Neural Networks for Visual Recognition
cs231n.stanford.eduProgressive GAN, Karras 2018. Models from Single RGB Images”, ECCV 2018 Beyond recognition: Segmentation, 2D/3D Generation. Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 1 - 15 March 30, 2021 Scene Graphs Krishna et al., Visual Genome: Connecting Vision and Language using Crowdsourced Image Annotations, IJCV 2017
Network, Visual, Recognition, Neural, Convolutional, Karar, Convolutional neural networks for visual recognition
Lecture 11: Detection and Segmentation
cs231n.stanford.eduFei-Fei Li & Justin Johnson & Serena Yeung Lecture 11 - 1 May 10, 2017 Lecture 11: Detection and Segmentation
Lecture 10: Recurrent Neural Networks
cs231n.stanford.eduimage -> sequence of words. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 13 May 4, 2017 Recurrent Neural Networks: Process Sequences e.g. Sentiment Classification sequence of words -> sentiment. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 14 May 4, 2017
Related documents
Wasserstein Generative Adversarial Networks
proceedings.mlr.pressWasserstein Generative Adversarial Networks the other hand, training GANs is well known for being del-icate and unstable, for reasons theoretically investigated in (Arjovsky & Bottou,2017). In this paper, we direct our attention on the various ways to measure how close the model distribution and the real dis-
Adversarial, Generative, Wasserstein, Wasserstein generative adversarial
ESRGAN: Enhanced Super-Resolution Generative Adversarial ...
openaccess.thecvf.comAbstract. The Super-Resolution Generative Adversarial Network (SR-GAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SR-
CartoonGAN: Generative Adversarial Networks for Photo ...
openaccess.thecvf.comtoonGAN, a generative adversarial network (GAN) frame-work for cartoon stylization. Our method takes unpaired photos and cartoon images for training, which is easy to use. Two novel losses suitable for cartoonization are pro-posed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of
Time-series Generative Adversarial Networks
papers.nips.ccA good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time ...
ffirstname.lastnameg@tue.mpg.de arXiv:2011.12100v2 [cs.CV ...
arxiv.orgGAN-based Image Synthesis: Generative Adversarial Networks (GANs) [24] have been shown to allow for pho-torealistic image synthesis at resolutions of 10242 pixels and beyond [6,14,15,39,40]. To gain better control over the synthesis process, many works investigate how factors of variation can be disentangled without explicit supervi-sion.
Conditional Image Synthesis with Auxiliary Classifier GANs
arxiv.orgGenerative adversarial networks (GANs) offer a distinct and promising approach that focuses on a game-theoretic formulation for training an image synthesis model (Good-fellow et al.,2014). Recent work has shown that GANs can produce convincing image samples on datasets with low variability and low resolution (Denton et al.,2015;Radford et al ...
Adversarial, Generative, Conditional, Generative adversarial
A Simple Unified Framework for Detecting Out-of ...
proceedings.neurips.ccdetecting adversarial samples in a sense, but do not utilize the Mahalanobis distance-based metric, i.e., they only utilize the Euclidean distance in their scores. In this paper, we show that Mahalanobis distance is significantly more effective than the Euclidean distance in various tasks. Experimental supports for generative classifiers.
“Deep Fakes” using Generative Adversarial Networks (GAN)
noiselab.ucsd.eduGenerative Adversarial Networks (GAN) The basic module for generating fake images is a GAN. A block diagram of a typical GAN network is shown in Fig-ure2. A GAN network is consisted of a generator and a discriminator. During the training period, we use a data set
Network, Using, Deep, Efka, Adversarial, Generative, Generative adversarial, Deep fakes using generative adversarial networks
Deep Learning on Graphs - Michigan State University
cse.msu.edu6.3 Graph Adversarial Defenses 151 6.3.1 Graph Adversarial Training 152 6.3.2 Graph Purification 154 6.3.3 Graph Attention 155 6.3.4 Graph Structure Learning 159 6.4 Conclusion 160 6.5 Further Reading 160 7 Scalable Graph Neural Networks 162 7.1 Introduction 162 7.2 Node-wise Sampling Methods 166 7.3 Layer-wise Sampling Methods 168