Facebook AI Research has developed a platform for carrying out dialog/textual language research. I haven't dug too deeply into it, but it looks like it ropes in common benchmark data sets as well as a hook into Amazon Mechanical Turk (commonly used for creating/evaluating research data) for a standardized research/evaluation/benchmarking interface. I haven't seen too much fanfare about this one, but maybe we'll start to see FAIR's later papers use more of this.
This is actually a follow-up release of code and data for a research paper released a while back, but it's a tremendously comprehensive release that's worth walking through.
In April, David Ha and Douglas Eck released a paper titled A Neural Representation of Sketch Drawings which described a network that sketched pictures of things. Rather than the usual ConvNet approach for generating images, this network instead draws sketches line by line sequentially, much as we might on paper. They mentioned then that they were using a data set from Quick, Draw! (more on that in a bit), but did not release the data. Never the less, the paper and the results were pretty interesting.
Now, we're getting the full release of the data set, as well as more exposure on Google's Magenta's research blog. For some context, Quick, Draw! was one of several Google A.I. experiments that was used to source various kinds of human input data, presented as tiny games/challenges. So it's nice that this data and research is now being made open to all to be expanded upon.
I've long thought that the big tech companies (and maybe other media companies too) have a big opportunity in making little apps and games with a large user-audience that can help them generate data for specific research projects. (In particular, I predict we're close to having a Turing-test kind of platform soon by either Google or Facebook, where humans and researcher-submitted agents alike interact and try to guess if their counterparts are human or AIs. I'm calling it now.) I suspect we'll be seeing many more of these experiments in the future.
We previously talked about Magenta and NSynth, a WaveNet-based instrument synthesizer. Magenta has released a new set of user-friendly tools for people to play with their NSynth, as well as additional analysis of their latent code representaton of instruments and sounds and other creative explorations of ways to "misuse" NSynth. The main downside of WaveNet remains that it is relatively slow to run, but this is nevertheless an interesting read, particularly for audiophiles.
A quick round-up of papers I really haven't had time to read (carefully):
Evaluating vector-space models of analogy
by Dawn Chen, Joshua C. Peterson, Thomas L. Griffiths
So we should all be familiar by know with the impressive property of word2vec-style word-embeddings that are able to do semantic analogy operations such as King - Man + Woman = Queen. While it's a nice property that shows up in every introductory text on word2vec, and word-embeddings have found tremendous applications in research, it's worth exploring further just how much information is captured in those embeddings. Researchers from Berkeley poked further at the power of those vector-based analogies, and found that while they work well for certain analogies, they completely fall apart for others. The authors run through several other forms of analogies that embeddings fall apart on, with generous use of mechanical turks for data-generation and evaluation. Worth a look, and certainly worth keeping in mind as we run up against the limits of word-embeddings and operating in the embedded latent space.
A Survey on Deep Learning in Medical Image Analysis
by Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
Neural Style Transfer: A Review
by Yongcheng Jing, Yezhou Yang, Zunlei Feng, Jingwen Ye, Mingli Song
Two review papers that I absolutely have not had the time to review. But review papers are always good for a quick recap and organization of common benchmarks and methods.
Outline Colorization through Tandem Adversarial Networks
by Kevin Frans
This was written by a high-schooler! I feel bad about all my life decisions now.
Contents of this post are intended for entertainment, and only secondarily for information purposes. All opinions, omissions, mistakes and misunderstandings are my own.