The theory of deep learning is now considered largely solved, and is well understood by researchers and influencers alike. To maintain our relevance, we therefore seek to apply our skills to under-explored, lucrative applications of this technology.
To this end, we propose and Deep Industrial Espionage, an efficient end-to-end framework for industrial information propagation and productisation. Specifically, given a single image of a product or service, we aim to reverse-engineer, rebrand and distribute a copycat of the product at a profitable price-point to consumers in an emerging market---all within in a single forward pass of a Neural Network. Differently from prior work in machine perception which has been restricted to classifying, detecting and reasoning about object instances, our method offers tangible business value in a wide range of corporate settings. Our approach draws heavily on a promising recent arxiv paper until its original authors names can no longer be read (we use felt tip pen). We then rephrase the anonymised paper, add the word novel to the title, and submit it a prestigious, closed-access espionage journal who assure us that someday, we will be entitled to some fraction of their extortionate readership fees.
We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical flow, to supervise representations of static images. The obvious approach of training a network to
predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose a much simpler learning goal: embed pixels such that the similarity between their embeddings matches that between their optical flow vectors. At test time, the learned deep network can be used without access to video or flow information and transferred to tasks such as image classification, detection, and segmentation. Our method, which significantly simplifies previous attempts at using motion for self-supervision, achieves state-of-the-art results in self-supervision using motion cues, competitive results for self-supervision in general, and is overall state of the art in self-supervised pretraining for semantic image segmentation, as demonstrated on standard benchmarks.
