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Hermann Grid. McCollough Effect. Impossible Triangle. Wundt Illusion. Hering Illusion. Orbison Illusion. Poggendorff Illusion. Vertical-Horizontal Illusion. Ponzo Illusion. Rubin's Vase.

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Young Woman or Old Woman. Book-Cleavage Ambiguous Figure. Coffer Illusion. Pointing Triangle. Sawtooth Ambiguous Figure. Schroeder's Stairs.

Neural networks don’t understand what optical illusions are

My Husband or My Father-in-Law. Impossible Cube. Penrose Stairs. Chicken-Church Ambiguous Figure. Kanizsa Triangle. Troxler Effect. Neon Color Spreading.

  1. Navigation menu.
  2. Hall of Illusions.
  3. Euro Crisis Aggregate Demand Control is European Single Currency Weakness?
  4. Buck Fuddies.
  5. Scintillating Grid. Watercolour illusion. Ehrenstein Figure. All Is Vanity. Disappearing Bust of Voltaire. Ambiguous ring. Impossible Corners. Grey strawberries. Which is where deep learning comes in.

    Optical Illusions and How They Work | AMNH

    In recent years, machines have learned to recognize objects and faces in images and then to create similar images themselves. Current machine-learning systems cannot generate their own optical illusions—at least not yet. Why not?

    1. How Do Optical Illusions Work?.
    2. White and gold or black and blue? That dress;
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    5. Visual Phenomena & Optical Illusions?
    6. The Hen?
    7. Akiyoshi's illusion pages.
    8. First some background. The recent advances in deep learning are based on two advances. The first is the availability of powerful neural networks and one or two programming tricks that make them good at learning.

      NEW Mystery Riddles and Illusions to Test Your Brain

      The second is the creation of huge annotated databases that machines can learn from. Teaching a machine to recognize faces, for example, requires many tens of thousands of images containing faces that are clearly labeled. With that information, a neural net can learn to spot characteristic facial patterns—two eyes, a nose, and a mouth, for example. And even more impressive, a pair of them—called a generative adversarial network—can teach each other to create realistic, but totally synthetic, images of faces.

      Williams and Yampolskiy set out to teach a neural network to identify optical illusions in the same way. The computing horsepower is easily available, but the necessary databases are not. That turns out to be hard. That represents a challenge for current machine-learning systems. So Williams and Yampolskiy compiled a database of over 6, images of optical illusions and then trained a neural network to recognize them.

      Motion & Time

      Then they built a generative adversarial network to create optical illusions for itself. The results were disappointing.

      1. Ninio's extinction illusion

      Nevertheless, this is an interesting result.