A system includes a neural network organized into layers corresponding to stages of inferences. The neural network includes a common portion, a first portion, and a second portion. The first portion includes a first set of layers dedicated to performing a first inference task on an input data. The second portion includes a second set of layers dedicated to performing a second inference task on the same input data. The common portion includes a third set of layers, which may include an input layer to the neural network, that are used in the performance of both the first and second inference tasks. The system may receive an input data and perform both inference tasks on the input data in a single pass. During training, a training sample with annotations for both inference tasks may be used to train the neural network in a single pass.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: one or more computing devices each comprising one or more processors and memory, the computing devices implementing a neural network comprising: a plurality of neurons configured to perform a plurality of inference tasks including a first inference task and a second inference task, the neurons organized in a plurality of layers corresponding to stages of inference made by the neural network; a first portion of the neural network comprising a first set of the plurality of layers including a first output layer configured to produce output for the first inference task performed on an input data, wherein output produced by the first set of layers are only used to perform the first inference task; a second portion of the neural network comprising a second set of the plurality of layers including a second output layer configured to produce output for the second inference task performed on the input data, wherein output produced by the second set of layers are only used to perform the second inference task; and a common portion of the neural network comprising a common set of the plurality of layers including an input layer configured to receive the input data, wherein the common set of layers produces output that are used to perform the plurality of inference tasks, including the first and the second inference tasks. 2. The system of claim 1, further comprising: a branch portion of the neural network distinct from the common portion, comprising a branch set of the plurality of layers, wherein the branch set of layers receives as input the output produced by the common portion and produces output that is used by the first portion to perform the first inference task and a third portion of the neural network to perform a third inference task, but not used by the second portion to perform the second inference task. 3. The system of claim 1, wherein: the input layer is configured to receive an input image; and the plurality of layers comprises one or more layers that correspond to respective sets of feature maps associated with features extracted from the image. 4. The system of claim 3, wherein: the common set of layers of the common portion comprises at least one layer that is a convolution layer; and the first set of layers of the first portion comprises at least one layer that is a deconvolution layer. 5. The system of claim 3, wherein the neural network is configured to perform a first inference task comprising an image classification task, and perform a second inference task comprising an image segmentation task. 6. The system of claim 3, further comprising: a sensor of an autonomous vehicle configured to capture images of road scenes; and a motion selector of the autonomous vehicle configured to receive outputs of the first and second inference tasks produced by the neural network and generate control directives to a motion control subsystem of the autonomous vehicle based at least in part on the outputs of the first and second inference tasks; and wherein the neural network receives the images captured by the sensor and performs the first and second inference tasks on the received images. 7. The system of claim 6, wherein the neural network is configured to produce an output of the first or second inference task, the output indicating a feature of the received image selected from the group consisting of a vehicle, a pedestrian, a road segment, or a lane. 8. A computer implemented method comprising: receiving an input data at an input layer of a multilayer neural network comprising a plurality of layers of neurons, each layer corresponding to an inference stage of the neural network; generating a common output by a common set of layers in the neural network, the common set of layers including the input layer; generating a first output associated with a first inference task by a first set of layers in the neural network based at least in part on the common output; and generating a second output associated with a second inference task by a second set of layers in the neural network based at least in part on the common output; wherein the first inference task is not performed using the second set of layers, and the second inference task is not performed using the first set of layers, and the first inference task and the second inference task are performed in single pass of the neural network. 9. The computer implemented method of claim 8, wherein: receiving the input data comprises receiving an input image; and generating the common output comprises generating one or more convolved feature maps associated with one or more respective features extracted from the input image; and generating the first output comprises generating one or more deconvolved feature maps associated with respective ones of the one or more convolved feature maps. 10. The computer implemented method of claim 9, wherein: generating the first output comprises performing an image classification task; and generating the second output comprises performing an image segmentation task. 11. The computer implemented method of claim 9, wherein: receiving the input image comprises capturing the input image using a sensor on an autonomous vehicle, the input image comprising an image of a road scene; and generating the first output comprises generating an output indicating a first road feature in the input image; generating the second output comprises generating an output indicating a second road feature in the input image; and further comprising: generating, by a motion selector of the autonomous vehicle, one or more control directives to a motion control subsystem of the autonomous vehicle that controls movement of the autonomous vehicle. 12. The computer implemented method of claim 11, wherein generating the first output or generating the second output comprises generating an indication of a road feature in the input image selected from the group consisting of a vehicle, a pedestrian, a road segment, or a lane. 13. A method comprising: providing a multilayer neural network comprising a plurality of neurons organized in layers, a first portion including a first set of layers generating output only for a first inference task, a second portion including a second set of layers generating output only for a second inference task, and a common portion including a common set of layers generating output for both the first and second inference tasks; feeding a training data sample to the neural network, the training data sample annotated with first ground truth labels for the first inference task and second ground truth labels for the second inference task; generating, by the neural network, first output for the first inference task and second output for the second inference task from the training data sample; updating first parameters in the first set of layers based at least in part on the first output but not based on the second output; updating second parameters in the second set of layers based at least in part on the second output but not based on the first output; and updating common parameters of the common set of layers based at least in part on both the first output and the second output. 14. The method of claim 13, further comprising: feeding a second training data sample to the neural network, the second training data sample annotated with ground truth labels for the first inference task but not ground truth labels for the second inference task; generating, by the neural network, an output for the first inference task from the second training data sample; generating a signal based at least in part on a determination that the second training data sample is not annotated with ground truth labels for the second inference task; updating the first parameters based at least in part on the output for the first inference task; and refraining from updating the second parameters based at least in part on the signal. 15. The method of claim 13, wherein updating the common parameters for the common set of layers comprises combining a first value and a second value, the first value being based at least in part on the first output and a first weight coefficient associated with the first inference task, and the second value being based at least in part on the second output and a second weight coefficient associated with the second inference task.