Dear AI-enthusiasts,

Please help me out by doing a fun & challenging task for science!

For my master’s thesis AI I’m researching fake images. I’m measuring how well people can distinguish fake from real.

Please consider filling out this survey, taking 5 to 10 minutes. Your answers are anonymous.

Important: it only works on a computer or tablet, not on a smartphone.

https://nils.limequery.com/427969?lang=en

Challenge: can you label all 18 images correctly? Until now, only 6 out of ~200 people managed to do so!

Thanks a lot in advance!

[ If you can no longer fill out the survey, the required number of respondents has been reached. ]

spectralGraphTopology provides estimators to learn k-component, bipartite, and k-component bipartite graphs from data by imposing spectral constraints on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices. Those estimators leverages spectral properties of the graphical models as a prior information, which turn out to play key roles in unsupervised machine learning tasks such as community detection.

spectralGraphTopology is developed on GitHub: https://github.com/dppalomar/spectralGraphTopology

For documentation, installation instructions, and examples of usage, check out https://mirca.github.io/spectralGraphTopology

https://medium.com/ai%C2%B3-theory-practice-business/new-high-quality-datasets-for-product-representation-learning-ac3ad1492bf4

Abstract: ProductNet is a collection of high-quality product datasets for better product understanding. ProductNet aims at supporting product representation learning by curating product datasets of high quality with properly chosen taxonomy. In this paper, the two goals of building high-quality product datasets and learning product representation support each other in an iterative fashion: the product embedding is obtained via a multi-modal deep neural network (master model) designed to leverage product image and catalog information; and in return, the embedding is utilized via active learning (local model) to vastly accelerate the annotation process. For the labeled data, the proposed master model yields high categorization accuracy (94.7% top-1 accuracy for 1240 classes), which can be used as search indices, partition keys, and input features for machine learning models. The product embedding, as well as the fined-tuned master model for a specific business task, can also be used for various transfer learning tasks.

Abstract: This manuscript presents some new impossibility results on adversarial robustness in machine learning, a very important yet largely open problem. We show that if conditioned on a class label the data distribution satisfies the `W_2` Talagrand transportation-cost inequality (for example, this condition is satisfied if the conditional distribution has density which is log-concave; is the uniform measure on a compact Riemannian manifold with positive Ricci curvature; etc.) any classifier can be adversarially fooled with high probability once the perturbations are slightly greater than the natural noise level in the problem. We call this result The Strong ”No Free Lunch” Theorem as some recent results (Tsipras et al. 2018, Fawzi et al. 2018, etc.) on the subject can be immediately recovered as very particular cases. Our theoretical bounds are demonstrated on both simulated and real data (MNIST). We conclude the manuscript with some speculation on possible future research directions.

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Paper:https://arxiv.org/pdf/1810.04065.pdf

I don’t have the necessary math background to understand the proofs in the paper. But I’m intrigued to intuitively understand what is the author saying.

Hi, I’m currently working on another project and I’m considering using transfer learning. I want to train a network with simple data that is easy to produce and use the pretrained network as a starting point for a more complicated model.

Eg. train a model to recognizing letters from images. Then somehow use another network to recognize words from a image.

Sure it is possible to create bounding boxes for each letter and then use the second network to predict them, but is there a way to apply transfer learning to a end to end approach?

Are there any papers in this area, any keywords that are describing these tasks.

I’m training an instance segmentation model and it’s not doing great, but tests indicate that with a larger dataset performance improves. I thus need to accelerate the process of generating bounding boxes and segmentation masks for new images. Are there tools which can help me? https://arxiv.org/pdf/1903.10830.pdf says they developed a pipeline to speedup annotation, but I don’t think they released any tool.