Abstract: A machine learning system is provided to enhance various aspects of machine learning models. In some aspects, a substantially photorealistic three-dimensional (3D) graphical model of an object is accessed and a set of training images of the 3D graphical mode are generated, the set of training images generated to add imperfections and degrade photorealistic quality of the training images. The set of training images are provided as training data to train an artificial neural network.

Abstract: A grammar is used in a grammatical evolution of a set of parent neural network models to generate a set of child neural network models. A generation of neural network models is tested based on a set of test data, where the generation includes the set of child neural network models. Respective values for each one of a plurality of attributes are determined for each neural network in the generation, where one of the attributes includes a validation accuracy value determined from the test. Multi-objective optimization is performed based on the values of the plurality of attributes for the generation of neural networks and a subset of the generation of neural network models is selected based on the results of the multi-objective optimization.

Abstract: A neural network model is trained, where the training includes multiple training iterations. Weights of a particular layer of the neural network are pruned during a forward pass of a particular one of the training iterations. During the same forward pass of the particular training iteration, values of weights of the particular layer are quantized to determine a quantized-sparsified subset of weights for the particular layer. A compressed version of the neural network model is generated from the training based at least in part on the quantized-sparsified subset of weights.

Abstract: A neural network model is trained, where the training includes multiple training iterations. Weights of a particular layer of the neural network are pruned during a forward pass of a particular one of the training iterations. During the same forward pass of the particular training iteration, values of weights of the particular layer are quantized to determine a quantized-sparsified subset of weights for the particular layer. A compressed version of the neural network model is generated from the training based at least in part on the quantized-sparsified subset of weights.

Abstract: A pruned version of a neural network is generated by determining pruned versions of each a plurality of layers of the network. The pruned version of each layer is determined by sorting a set of channels of the layer based on respective weight values of each channel in the set. A percentage of the set of channels are pruned based on the sorting to form a thinned version of the layer. Accuracy of a thinned version of the neural network is tested, where the thinned version of the neural network includes the thinned version of the layer. The thinned version of the layer is used to generate the pruned version of the layer based on the accuracy of the thinned version of the neural network exceeding a threshold accuracy value. A pruned version of the neural network is generated to include the pruned versions of the plurality of layers.

Abstract: A pruned version of a neural network is generated by determining pruned versions of each a plurality of layers of the network. The pruned version of each layer is determined by sorting a set of channels of the layer based on respective weight values of each channel in the set. A percentage of the set of channels are pruned based on the sorting to form a thinned version of the layer. Accuracy of a thinned version of the neural network is tested, where the thinned version of the neural network includes the thinned version of the layer. The thinned version of the layer is used to generate the pruned version of the layer based on the accuracy of the thinned version of the neural network exceeding a threshold accuracy value. A pruned version of the neural network is generated to include the pruned versions of the plurality of layers.

Abstract: Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement video surveillance with neural networks are disclosed. Example systems disclosed herein include a database to store records of operator-labeled video segments (e.g., as records of operator-labeled video segments). The operator-labeled video segments include reference video segments and corresponding reference event labels describing the video segments. Disclosed example systems also include a neural network including a first instance of an inference engine, and a training engine to train the first instance of the inference engine based on a training set of the operator-labeled video segments obtained from the database, the first instance of the inference engine to infer events from the operator-labeled video segments included in the training set.

Abstract: An output of a first one of a plurality of layers within a neural network is identified. A bitmap is determined from the output, the bitmap including a binary matrix. A particular subset of operations for a second one of the plurality of layers is determined to be skipped based on the bitmap. Operations are performed for the second layer other than the particular subset of operations, while the particular subset of operations are skipped.

Abstract: Methods, apparatus, systems and articles of manufacture to reconstruct scenes using convolutional neural networks are disclosed. An example apparatus includes a sensor data acquirer to acquire ground truth data representing an environment, an environment detector to identify an environmental characteristic of the environment, a synthetic database builder to apply noise to the ground truth data to form a training set, a model builder to train a machine learning model using the training set and the ground truth data, and a model adjustor to modify the machine learning model to include residual OR-gate connections intermediate respective layers of the machine learning model. The synthetic database builder is further to store the machine learning model in association with the environmental characteristic of the environment.

Abstract: A machine learning system is provided to enhance various aspects of machine learning models. In some aspects. a substantially photorealistic three-dimensional (3D) graphical model of an object is accessed and a set of training images of the 3D graphical mode are generated, the set of training images generated to add imperfections and degrade photorealistic quality of the training images. The set of training images are provided as training data to train an artificial neural network.

Abstract: Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.

Abstract: A ray is cast into a volume described by a volumetric data structure, which describes the volume at a plurality of levels of detail. A first entry in the volumetric data structure includes a first set of bits representing voxels at a lowest one of the plurality of levels of detail, and values of the first set of bits indicate whether a corresponding one of the voxels is at least partially occupied by respective geometry. A set of second entries in the volumetric data structure describe voxels at a second level of detail, which represent subvolumes of the voxels at the first lowest level of detail. The ray is determined to pass through a particular subset of the voxels at the first level of detail and at least a particular one of the particular subset of voxels is determined to be occupied by geometry.

Abstract: An output of a first one of a plurality of layers within a neural network is identified. A bitmap is determined from the output, the bitmap including a binary matrix. A particular subset of operations for a second one of the plurality of layers is determined to be skipped based on the bitmap. Operations are performed for the second layer other than the particular subset of operations, while the particular subset of operations are skipped.

Abstract: An example stationary tracker includes memory to store fixed geographic location information indicative of a fixed geographic location of the stationary tracker, and to store a reference feature image; and at least one processor to: determine a feature in an image is a non-displayable feature by comparing the feature to the reference feature image; and generate a masked image, the masked image to mask the non-displayable feature based on the non-displayable feature not allowed to be displayed when captured from the fixed geographic location of the stationary tracker, and the masked image to display a displayable feature in the image; and a wireless interface to detect a wireless tag located on a tag bearer, the at least one processor to determine the tag bearer is the displayable feature in the image based on the wireless tag.

Abstract: A ray is cast into a volume described by a volumetric data structure, which describes the volume at a plurality of levels of detail. A first entry in the volumetric data structure includes a first set of bits representing voxels at a lowest one of the plurality of levels of detail, and values of the first set of bits indicate whether a corresponding one of the voxels is at least partially occupied by respective geometry. A set of second entries in the volumetric data structure describe voxels at a second level of detail, which represent subvolumes of the voxels at the first lowest level of detail. The ray is determined to pass through a particular subset of the voxels at the first level of detail and at least a particular one of the particular subset of voxels is determined to be occupied by geometry.

Abstract: An output of a first one of a plurality of layers within a neural network is identified. A bitmap is determined from the output, the bitmap including a binary matrix. A particular subset of operations for a second one of the plurality of layers is determined to be skipped based on the bitmap. Operations are performed for the second layer other than the particular subset of operations, while the particular subset of operations are skipped.

Abstract: A volumetric data structure models a particular volume representing the particular volume at a plurality of levels of detail. A first entry in the volumetric data structure includes a first set of bits representing voxels at a first level of detail, the first level of detail includes the lowest level of detail in the volumetric data structure, values of the first set of bits indicate whether a corresponding one of the voxels is at least partially occupied by respective geometry, where the volumetric data structure further includes a number of second entries representing voxels at a second level of detail higher than the first level of detail, the voxels at the second level of detail represent subvolumes of volumes represented by voxels at the first level of detail, and the number of second entries corresponds to a number of bits in the first set of bits with values indicating that a corresponding voxel volume is occupied.

Abstract: A particular voxel is identified within a volume and a hash table is used to obtain volumetric data describing the particular voxel within the volume. Values of x-, y- and z-coordinates in the volume associated with the particular voxel are determined an index value associated with the particular voxel is determined according to a hashing algorithm, where the index value is determined from summing weighted values of the x-, y- and z-coordinates, and the weighted values are based on a variable value corresponding to a dimension of the volume. A particular entry is identified in the hash table based on the index value, where the particular entry includes volumetric data, and the volumetric data identifies, for the particular voxel, whether the particular voxel is occupied.

Abstract: An example stationary tracker includes: memory to store fixed geographic location information indicative of a fixed geographic location of the stationary tracker, and to store a reference feature image; and at least one processor to: determine a feature in an image is a non-displayable feature by comparing the feature to the reference feature image; and generate a masked image, the masked image to mask the non-displayable feature based on the non-displayable feature not allowed to be displayed when captured from the fixed geographic location of the stationary tracker, and the masked image to display a displayable feature in the image.

Abstract: A ray is cast into a volume described by a volumetric data structure, which describes the volume at a plurality of levels of detail. A first entry in the volumetric data structure includes a first set of bits representing voxels at a lowest one of the plurality of levels of detail, and values of the first set of bits indicate whether a corresponding one of the voxels is at least partially occupied by respective geometry. A set of second entries in the volumetric data structure describe voxels at a second level of detail, which represent subvolumes of the voxels at the first lowest level of detail. The ray is determined to pass through a particular subset of the voxels at the first level of detail and at least a particular one of the particular subset of voxels is determined to be occupied by geometry.