We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classifi...
Lecture 12 | Visualizing and Understanding 01:15:48 Lecture 13 | Generative Models 01:17:41 Lecture 14 | Deep Reinforcement Learning 01:04:01 Lecture 15 | Efficient Methods and Hardware for Deep Learning 01:16:52 Lecture 16 | Adversarial Examples and Adversarial Training 01:21:46 Lectur...
Generating Natural Language Adversarial Examples on a Large Scale with Generative ModelsJianbin LinJun ZhouShuang YangSiliang TangXiang RenYankun RenYuan Qi
This is in contrast to unconditional models (also calledgenerative models), used to analyze the joint distribution of inputs and outputs. Regression vs classification There are two classes of conditional models: regression models, in which the output variable is continuous; for example: thelinear re...
My idea is that everyone is able to follow every line of the code, and run the experiments within a couple of minutes on almost any laptop or computer. My goal is to encourage people who are new to understand and play with deep generative models. More advanced users, on the other hand...
Loss of ensemble models(Liu等人,2017)首先提出了通过利用具有不同架构的多个模型的集合来生成对抗性示例的可迁移性。(Gubri等人,2022 b)提出了一种几何方法,通过引入大几何邻域(LGV)来增强黑盒对抗攻击的可迁移性。LGV从传统训练的深度神经网络开始,通过沿着SGD轨迹以高恒定学习率收集权重来构建替代模型。(Gubri...
Although other security issues pertaining to confidentiality and privacy have been drawn attention in deep learning [45, 46, 47], we focus on the attacks that degrade the performance of deep learning models, cause an increase of false positives and false negatives. • The rest of the threat ...
Generative Adversarial Networks (DCGAN) Variational Auto-Encoders Superresolution using an efficient sub-pixel convolutional neural network Hogwild training of shared ConvNets across multiple processes on MNIST Training a CartPole to balance in OpenAI Gym with actor-critic Natural Language Inference (SNLI)...
Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing. Model matchmaking: Choose a suitable generative AI model for your specific needs. Teamwork: Assemble a team with expertise in AI, data science and your industry. This interdisciplinary...
A major breakthrough was the introduction ofnatural language processing (NLP), enabling AI-powered educational assistants to interact with students in natural language. The late 2010s saw the emergence of generative AI models like GPT, revolutionizing the field by generating educational content, providi...