![]() We then built Panorama to mitigate the unbounded vocabulary problem and designed it to be domain-agnostic and efficient. If the practitioner already has a model in deployment, Panorama can also leverage it for week supervision. We also devised an auto-training scheme for setting the hyper-parameters related to short-circuiting criteria. We developed a novel deeply supervised cascade architecture based on this observation. But do we need all that depth, all that capabilities for more manageable tasks and queries? A solution to it is cascaded processing, where the model is divided into stages and can execute early-exiting or short-circuiting whenever possible. Deeply supervised cascade processingĭeep nets can have over one hundred layers and very expensive due to the sheer amount of FLOPS required. So the object classification becomes a simple nearest neighbor search, and the vocabulary can be expanded on-the-fly. Embeddings of the same entity then gather closer than those of different entities. ![]() ![]() Instead of training the deep net for predicting labels, we train the model to yield discriminative features called embeddings. To mitigate the unbounded vocabulary issue, one can resort to a technique known as embedding extraction or metric learning. To re-train it, you need data science expertise and may need to collect data for each component of it. That state-of-art model for your task can be bespoke and complicated, compositing a dozen sub-models for the final task. The efforts and expertise required to alter the vocabulary can be tremendous. It’s not easy to keep up and refresh your model often. New car models released every fortnight, new animal species discovered from time to time, people come and go, constantly. Unlike static lab environments where the machine learning models were built, the real world is dynamic, and the vocabulary keeps changing. Without knowing what the car is, it probably picks the closest pony car rival. And the vocabulary of the training data becomes its fixed vocabulary. Just like us, your model needs a vocabulary to name things. This means it can never figure out the correct answer for any Dodge Challenger. In fact, out of the 431 car models it can predict, there wasn’t even Dodge Challenger since it wasn’t in the training dataset. After all, you can never trust these machine learning models.īut there’s more to it. It believes this Dodge Challenger is in fact a Ford Mustang, 91% sure. Or sometimes flaky on a Dodge Challenger: You can download a pre-trained model and it can do the work just fine on Ford Mustang and Chevy Camaro: Say you got an application for car model recognition, where you want to identify the models and makes of cars in a traffic camera video. It looks all you have to do is downloading and using them. Face recognition, traffic monitoring, video analytics most likely, there are already some pre-trained weights. ![]() It’s also quite convenient to download a cutting-edge deep learning model from GitHub or bitbucket for your application. There is a technical report with all the details, and source code/demos on github.ĭeep learning has become more popular than ever. This is a post about our research project, Panorama.
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