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Look up adversarial noise. That's a technology that can fool SOTA methods.

• kevin_thibedeau 6y ago
  For specific models.
• baq 6y ago
  always wondered why a low pass filter isn't a standard part of the training pipeline?
  • gugagore 6y ago
    The early convolution layers could implement a low-pass filter with the appropriate weights. Presumably the learning algorithm would do so if it were beneficial.
    • baq 6y ago
      And yet there are tools to confuse networks with high frequency artifacts. If the network isn’t trained to ignore that, it won’t - but you don’t need a neural network to perform a low pass filter step if you can do that efficiently before asking the net what it sees on the already preprocessed image.
• KMnO4 6y ago
  Adversarial attacks rely on a specific model’s gradient (since you’re essentially trying to find the most sensitive pixels). Adversarial noise that affects model A won’t necessarily work on model B. That said, most people transfer train from well trained nets (ImageNet, Inception, etc). Finally, not all SOTA methods are susceptible to adversarial attacks, eg capsule networks.
  • throw99901 6y ago
    >Finally, not all SOTA methods are susceptible to adversarial attacks, eg capsule networks. They appear to be susceptible: https://arxiv.org/pdf/1906.03612.pdf https://arxiv.org/pdf/1906.03612.pdf
    • KMnO4 6y ago
      That’s neat; hadn’t seen that paper. Thanks for sharing.