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Found 8 papers, 4 papers with code
Neighbourhood Distillation: On the benefits of non end-to-end distillation
Instead of distilling a model end-to-end, we propose to split it into smaller sub-networks - also called neighbourhoods - that are then trained independently.
Fine-Grained Stochastic Architecture Search
Mobile neural architecture search (NAS) methods automate the design of small models but state-of-the-art NAS methods are expensive to run.
Sky Optimization: Semantically aware image processing of skies in low-light photography
The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture.
Structured Multi-Hashing for Model Compression
Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory.
Synthetic Depth-of-Field with a Single-Camera Mobile Phone
Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background.
No Fuss Distance Metric Learning using Proxies
Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized.
How useful is photo-realistic rendering for visual learning?
Data seems cheap to get, and in many ways it is, but the process of creating a high quality labeled dataset from a mass of data is time-consuming and expensive.
Ontological Supervision for Fine Grained Classification of Street View Storefronts
Modern search engines receive large numbers of business related, local aware queries.
