MULTI-OBJECT DETECTION WITH SINGLE DETECTION PER OBJECT

1 . A method comprising:

receiving a training batch of data samples comprising a plurality of labeled classifications;

extracting features from the data sample to generate a batch of features;

processing the batch of features using a neural network to generate one or more classifications for each data sample;

computing a rhino loss value for the training batch; and

modifying weights of the neural network to reduce the rhino loss value.

2 . The method of claim 1 , wherein the training batch includes a plurality of speech utterances and computing the rhino loss value further comprises generating the rhino loss value for a plurality of speakers.

3 . The method of claim 1 , wherein processing the batch of features using a neural network to generate one or more classifications for each data sample, further comprises identifying one or more objects in each sample with a single-classification per object.

4 . The method of claim 1 , wherein the training batch comprises a plurality of audio samples comprising a first number of speakers and a second number of audio samples per speaker.

5 . The method of claim 4 , wherein the classification comprises phoneme recognition in a stream of audio samples.

6 . The method of claim 1 , further comprising a speaker authentication process comprising:

receiving a target audio signal comprising speech from a target speaker;

extracting target features from the target audio signal;

processing the target features through the neural network to generate one or more user classifications; and

determining whether the target speaker is associated with a user identifier based at least in part on the one or more user classifications;

wherein determining whether the target speaker is associated with a user identifier comprises calculating a confidence score measuring a strength of a classification determination.

7 . The method of claim 1 , wherein the training batch comprise a plurality of images including object classification labels.

8 . The method of claim 7 , wherein processing the batch of features using a neural network to generate one or more classifications for each data sample comprises producing an object detection classification activation in one grid cell determined responsible for detecting the classified object.

9 . The method of claim 7 , wherein computing the rhino loss value further comprises generating the rhino loss value for a plurality of object classifications.

10 . The method of claim 7 , wherein processing the batch of features using a neural network to generate one or more classifications for each data sample comprises detecting and localizing an object in an image with one bounding box per object using a single-stage object detector.

11 . A system comprising:

a logic device configured to train a neural network using a rhino loss function, the logic device configured to execute logic comprising:

receiving a training batch of labeled data samples;

extracting features from the data samples to generate a batch of features;

processing the batch of features using a neural network to generate classifications configured to classify the data samples;

computing a rhino loss value for the training batch based, at least in part, on the classifications; and

modifying weights of the neural network to reduce the rhino loss value.

12 . The system of claim 11 , wherein computing the rhino loss value further comprises calculating the rhino loss value for a plurality of speakers, based at least in part on the classifications.

13 . The system of claim 14 , wherein processing the batch of features using a neural network to generate one or more classifications for each data sample, further comprises identifying one or more objects in each sample with a single-classification per object.

14 . The system of claim 11 , wherein the logic device is further configured to execute logic comprising a backbone network comprising a pre-trained image classifier configured to learn how to extract features from the image.

15 . The system of claim 11 , wherein the logic device is further configured to execute logic comprising a backbone network configured for phoneme recognition; wherein each output is a confidence score for a probability of detecting a class and it is obtained after applying a sigmoid function.

16 . A system comprising:

a logic device configured to train a neural network for a classification task by executing logic comprising:

receiving a training dataset comprising labeled training data samples;

pre-training a backbone architecture as a classifier using the training dataset;

extracting feature maps from an intermediate layer of the backbone architecture; and

identifying a portion of each data sample that relates to the extracted feature maps.

17 . The system of claim 16 , wherein the training dataset comprises a plurality of images and wherein the logic device is further configured to execute logic comprising subdividing each image into a plurality grid cells and identifying which of the plurality of grid cells relates to a center of a bounding box annotation for the image.

18 . The system of claim 17 , wherein the image includes a plurality of objects, and wherein the logic device is further configured to execute logic comprising generating a single activation for each of the detected objects.

19 . The system of claim 16 , wherein the training dataset comprises a plurality of audio samples comprising a plurality of frames, and wherein the logic device is further configured to execute logic comprising identifying a phenome by identifying frames that relate to a phenome activation.

20 . The system of claim 16 , wherein identifying a portion of each data sample that relates to the extracted feature maps comprises using a neural network to generate one or more classifications for each data sample comprises detecting and localizing an object in an image with one bounding box per object using a single-stage object detector.