University of Central Florida Courses

Adaptive Attacks to Adversarial Example Defenses - CAP6412 Spring 2021

Explore adaptive attacks on adversarial example defenses, covering expectation over transformation, K winners take all, chaos partitioning, and local gradient estimation.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Improving Transferability of Adversarial Examples with Input Diversity - CAP6412 Spring 2021

Explore techniques to enhance adversarial example transferability using input diversity, including FGSM family methods and ensemble network attacks.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Universal Adversarial Perturbations in Machine Learning - Spring 2021

Explore universal adversarial perturbations in machine learning, covering definitions, contributions, experiments, and cross-model universality.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Explaining and Harnessing Adversarial Examples in Machine Learning - Spring 2021

Explore adversarial examples in machine learning, their impact on linear and non-linear models, and techniques like adversarial training to improve model robustness and generalization.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Intriguing Properties of Neural Networks - Spring 2021

Explore intriguing properties of neural networks, including adversarial attacks, transfer, training overlap, and semantic information in units. Analyze weaknesses and spectral instability.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Introduction to Adversarial Attacks in Machine Learning - Lecture 1

Introduction to adversarial attacks on AI systems, covering real-world examples, attack methods, and defense strategies for various applications like face recognition and object detection.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Fast Is Better Than Free: Revisiting Adversarial Training - CAP6412 Spring 2021

Explore adversarial training techniques, comparing PGD with newer methods. Learn about technical contributions, experimental results, and implications for machine learning security.

• YouTube
• 34 minutes
• On-Demand
• Free Video

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Explore techniques for interpreting and explaining deep learning models, focusing on LRP rules, Taylor decomposition, and properties of effective explanations in AI systems.

• YouTube
• 32 minutes
• On-Demand
• Free Video

CAP6412 - SmoothGrad: Removing Noise by Adding Noise - Lecture

Explore SmoothGrad, a technique for enhancing neural network visualization by adding noise to input images and averaging resulting sensitivity maps.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Cross-Domain Transferability of Adversarial Perturbations - CAP6412 Spring 2021

Explore cross-domain transferability of adversarial perturbations, focusing on generative models, discriminator loss, and experimental results in this advanced computer vision lecture.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Interpretable Explanations of Black Boxes by Meaningful Perturbation - CAP6412 Spring 2021

Explore principles and methods for explaining black box functions in machine learning, focusing on image saliency, perturbation techniques, and meta-predictors for interpretable AI.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Unmasking Clever Hans Predictors and Assessing Machine Learning - Spring 2021

Unmasking clever predictors: Explore Layer-wise Relevance Propagation and SPRAY techniques to assess machine learning models' decision-making processes and identify potential biases or faults.

• YouTube
• 47 minutes
• On-Demand
• Free Video

How Can I Explain This to You? An Empirical Study of Deep Neural Net Explanation Methods - Spring 2021

Empirical study comparing deep neural network explanation methods, examining usability, stability, and privacy risks across various visual explanation frameworks.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Robust Pre-Training by Adversarial Contrastive Learning - CAP6412 Spring 2021

Explore robust pre-training techniques using adversarial contrastive learning, focusing on unsupervised and semi-supervised methods for improved model resilience and accuracy.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Adversarial Self-Supervised Contrastive Learning

Explore adversarial self-supervised contrastive learning, covering transformers, frameworks, algorithms, evaluations, and experiments in this comprehensive presentation.

• YouTube
• 43 minutes
• On-Demand
• Free Video