Dexa/Machine Learning Street Talk (MLST)

Adversarial Examples Complexity

Tim delves into the complexity of adversarial examples, highlighting the challenges in defending against them. He discusses defense mechanisms like data augmentation and adversarial training, shedding light on their effectiveness and limitations in combating adversarial attacks in machine learning models.

In this clip
From this podcast
Machine Learning Street Talk (MLST)
#040 - Adversarial Examples (Dr. Nicholas Carlini, Dr. Wieland Brendel, Florian Tramèr)
Related Questions
- How can we defend against adversarial attacks on machine learning models as discussed in the episode #52 - Unadversarial Examples (Hadi Salman, MIT) and the clip Neural Network Features?
- How can we defend against adversarial attacks on machine learning models?