We introduce a new type of Deep Belief Net and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB database(normalized-uniform version), which contains stereo-pair images of objects under different lighting conditions and viewpoints. Our model achieves 6.5% error on the test set, which is close to the best published result for NORB (5.9%) using a convolutional neural net that has built-in knowledge of translation invariance. It substantially outperforms shallow models such as SVMs (11.6%).

We introduce a new type of Deep Belief Net and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB database(normalized-uniform version), which contains stereo-pair images of objects under different lighting conditions and viewpoints. Our model achieves 6.5% error on the test set, which is close to the best published result for NORB (5.9%) using a convolutional neural net that has built-in knowledge of translation invariance. It substantially outperforms shallow models such as SVMs (11.6%).

Now Warden's tackling a new challenge: teaching smartphones (and cameras in general) how to recognize objects. Back in April, Warden released a software development kit called DeepBeliefSDK on GitHub. Designed for developers to integrate machine vision into smartphone apps, DeepBelief is currently available for Android, iOS, Linux, and Raspberry Pi. While DeepBelief is just one in a number of early entrants into the somewhat creepy world of deep learning for mobile devices, it has one advantage over competitors. It's blazing fast–Warden says Deep Belief can identify objects in under 300 milliseconds on an iPhone 5S, while using less than 20 megabytes of memory.