To produce these images, Dr. David Nutt, lead on the study at Imperial College London, got 20 people doped up. Using three kinds of neural imaging — arterial spin labeling, resting state MRI and magnetoencephalography — Nutt’s team found changes in brain blood flow, increased electrical activity and a big communication spike in the parts of your brain that handle vision, motion, hearing and awareness. Here’s what the study proves.
The fact that humans report that Google’s Inceptionism looks to them like what they see when they hallucinate on LSD or other drugs suggests that the machinery ‘under the hood’ in our brains is similar in some way to deep neural networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Computer Science academic paper by Alec Radford, Luke Metz, and Soumith Chintala explores neural network method of generating new forms from huge image datasets, particularly human faces and interior rooms:
In recent years, supervised learning with convolutional networks (CNNs) has
seen huge adoption in computer vision applications. Comparatively, unsupervised
learning with CNNs has received less attention. In this work we hope to help
bridge the gap between the success of CNNs for supervised learning and
unsupervised learning. We introduce a class of CNNs called deep convolutional
generative adversarial networks (DCGANs), that have certain architectural
constraints, and demonstrate that they are a strong candidate for unsupervised
learning. Training on various image datasets, we show convincing evidence that
our deep convolutional adversarial pair learns a hierarchy of representations
from object parts to scenes in both the generator and discriminator.
Additionally, we use the learned features for novel tasks - demonstrating their
applicability as general image representations.
An image recognition network dreams about every object it knows. Part 2/2: non-animals
Second video from Ville-Matias Heikkilä uses deep-dream like technique to visually reveal a collected neural dataset, this time featuring man-made objects and food:
Network used: VGG CNN-S (pretrained with Imagenet)
There are 1000
output neurons in the network, one for each image recognition category.
In this video, the output of each of these neurons is separately
amplified using backpropagation (i.e. deep dreaming).
line shows the category title of the amplified neuron. The bottom line
shows the category title of the highest competing neuron. Color coding:
green = amplification very succesful (second tier far behind), yellow =
close competition with the second tier, red = this is the second tier.