teamLab: Blurring the Boundaries between Art and Science

My Lizard Painting

Yesterday, we visited MORI building digital art museum: teamLab Borderless (This name is quite long and too hard to remember in the right order :P) which was opened recently in Odaiba... to make Odaiba, or Tokyo for that matter, even greater! 

Even though some exhibits look a bit trivial in the beginning, (I felt that the exhibition ticket was somewhat over priced, although it was at discounted price and regardless of the fact that we did not pay for it), a second thought after further reading made me feel so overwhelmed, impressed and fascinated about the extent of innovation, creativity and philosophical thoughts that they have put together in to each piece of art.

This museum gives us a great feel of how digital involvement can nicely complement the traditional forms of art and overcome their inherent limitations. The museum is based on few great concepts. One such concept that highly captivated my curious (... well, about perception, in its all forms) mind is their notion on ultra subjective space. Comparing that concept with the western perspective of paintings and ancient Japanese spatial recognition made the idea even more lucrative.

If you are planning to visit this museum, I highly recommend that  you understand those concepts before you visit the museum, in addition to other "things you should read before you visit", to make your museum experience even better.

On the other hand, some activities were quite fun too. Look at how I painted a cute lizard and the way it came alive a little later with all those "natural lizard like moves"!

Network Dissection to Divulge the Hidden Semantics of CNN

Needless to mention that nowadays deep convolutional neural networks (CNNs) have gained immense popularity due to its ability to classify or recognize scenes or objects with reasonable accuracy. However, we already know that CNNs can also be fooled by adversarial attacks, so that a given image, that was accurately recognized by a CNN earlier, can be altered in a way that even though its still possible for a human to recognize well, CNN would fail to do so [1]. So, the natural question arises "Are they genuinely learning about object or scenes like we humans do?"

Dissection

Researchers from MIT have recently conducted some experiments along that line as what's happening in hidden layers of CNNs still remains a mystery [2]. Their experiments aim to find out if those individual hidden units align with some human interpretable concepts such as parts of an object or objects in a scene. E.g., lamps (object detector unit) in place recognition, bicycle wheel (part detector) in object detection. If so, they need to find a way to quantify the emerged 'interpretability'. It's interesting to know that neurologists perform a similar task to uncover the behavior of biological neurons too.

Researchers have conducted experiments to find which factors (E.g., axis representation, training techniques) influences to interpretability of those hidden units too. They have found that interpretability is axis dependent, in the sense that if you change the rotation of a given image, the hidden units will no longer be interpretable. Further, different training techniques such as dropout or batch normalization have an impact on interpretability too.

You can find more details on this research here.

[1] https://kaushalya.github.io/DL-models-resistant-to-adversarial-attacks/
[2] D. Bau*, B. Zhou*, A. Khosla, A. Oliva, and A. Torralba. "Network Dissection: Quantifying Interpretability of Deep Visual Representations." Computer Vision and Pattern Recognition (CVPR), 2017. Oral.

Taskonomy: Disentangling Task Transfer Learning

image source: Taskonomy [1]

The common computer vision tasks such as  depth estimation, edge detection are usually performed in isolation.

While scanning through this year’s CVPR papers, I noticed this interesting research [1] (CVPR Best Paper award winner!) that introduced a term called “Taskonomy” (Task + Taxonomy).

Taskonomy focuses on deriving the relationships between these common computer vision tasks so that it can find some representations obtained by certain computer vision tasks that can be useful (in terms of efficient computation time and/ or requirement for less labeled data) in other computer vision tasks.

This is also known as ‘task transferability’.  Some interesting visualizations and more information on this research can be found here.

[1] http://taskonomy.stanford.edu/

Seeing is 'Not Necessarily' Believing?

Recently, I read the Nature article, “Our useful inability to see the reality”that introduced a book called “Deviate: The Science of Seeing Differently”. I haven’t read the book yet, but this seems to shed some light on the idea of perception. The basic idea seems to be that we see what we want to see, based on our past experience, and not necessarily what’s out there in reality. In other words, the information we acquire from our eyes has much less to do with how we derive the actual meaning of it (in relation to ourselves of course). Specifically, it is being discovered that the 90% of the neurons that are responsible to make sense of what we see don’t consist of the visual fields in the brain.