Apparatuses and methods of manufacturing same, systems, and methods for training deep learning machines are described. In one aspect, candidate units, such as detection bounding boxes in images or phones of an input audio feature, are classified using soft labelling, where at least label has a range of possible values between 0 and 1 based, in the case of images, on the overlap of a detection bounding box and one or more ground-truth bounding boxes for one or more classes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for deep learning training, comprising: receiving candidate units for classification; and classifying the candidate units by soft labelling, wherein soft labelling provides at least one label comprising a plurality of possible values in a range between 0 and 1. 2. The method of claim 1, wherein the candidate units are detection bounding boxes within an image or phones of an input audio feature. 3. The method of claim 1, wherein the candidate units are detection bounding boxes, and wherein soft labelling comprises: providing a label of a class for a detection bounding box based at least partially on an overlap of the detection bounding box with a ground-truth bounding box for the class. 4. The method of claim 3, wherein providing a label of a class comprises: assigning a class label whose value is derived using the area of the overlap of the detection bounding box with the ground-truth bounding box for the class. 5. The method of claim 3, wherein providing a label of a class comprises: assigning a class label whose value is derived from a ratio involving the area of the overlap of the detection bounding box with the ground-truth bounding box for the class. 6. The method of claim 5, wherein assigning a class label comprises: calculating the ratio of the area of the overlap of the detection bounding box with the ground-truth bounding box for the class to the entire area of the detection bounding box. 7. The method of claim 3, wherein providing a label of a class is also based on one or more threshold values. 8. The method of claim 7, wherein providing a class label comprises: assigning a class label of 0 if a value based on the area of the overlap of the detection bounding box with the ground-truth bounding box for the class is below a first threshold value; assigning a class label of 1 if the value based on the area of the overlap of the detection bounding box with the ground-truth bounding box for the class is above a second threshold value and if the value based on the area of the overlap of the detection bounding box with the ground-truth bounding box for the class is the first threshold value, the second threshold value, or between the first and second threshold value, assigning a class label of the value based on the area of the overlap of the detection bounding box with the ground-truth bounding box for the class. 9. The method of claim 8, wherein the value based on the area of the overlap of the detection bounding box with a ground-truth bounding box for the class is a ratio of the area of the overlap of the detection bounding box with the ground-truth bounding box for the class to the entire area of the detection bounding box. 10. The method of claim 3, wherein providing a label of a class for a detection bounding box is also based on one or more confidence levels provided by a detection stage which also provided the detection bounding box. 11. The method of claim 3, wherein providing a label of a class for a detection bounding box comprises: providing a label of a first class for the detection bounding box based at least partially on the overlap of the detection bounding box with a ground-truth bounding box for the first class; and providing a label of a second class for the detection bounding box based at least partially on the overlap of the detection bounding box with a ground-truth bounding box for the second class. 12. The method of claim 11, wherein there is an overlap of the detection bounding box, the ground-truth bounding box for the first class, and the ground-truth bounding box for the second class, and wherein the first class label and the second class label are also based on the overlap of the detection bounding box, the ground-truth bounding box for the first class, and the ground-truth bounding box for the second class. 13. The method of claim 1, wherein the candidate units are phones of an input audio feature, and wherein soft labelling comprises: generating soft labels directly from classification scores from a probability model or neural network. 14. The method of claim 1, wherein the candidate units are phones of an input audio feature, and wherein soft labelling comprises: generating soft labels directly from classification scores from a hidden Markov Model (HMM), a Gaussian mixture model (GMM), or a pretrained neural network. 15. The method of claim 1, wherein the candidate units are phones of an input audio feature, and wherein soft labelling comprises: generating soft labels using maximum likelihood decoding, a distance metric, a soft output decoding algorithm, or a list decoding scheme. 16. An apparatus for deep learning training, comprising: one or more non-transitory computer-readable media; and at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs the steps of: receiving candidate units for classification; and classifying the candidate units by soft labelling, wherein soft labelling provides at least one label comprising a plurality of possible values in a range between 0 and 1. 17. The apparatus of claim 16, wherein the candidate units are detection bounding boxes, and wherein soft labelling comprises: providing a label of a class for a detection bounding box based at least partially on an overlap of the detection bounding box with a ground-truth bounding box for the class 18. The apparatus of claim 16, wherein the candidate units are phones of an input audio feature, and wherein soft labelling comprises: generating soft labels directly from classification scores from a probability model or neural network. 19. A method, comprising: manufacturing a chipset capable of deep learning training comprising: at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs the steps of: receiving candidate units for classification; and classifying the candidate units by soft labelling, wherein soft labelling provides at least one label comprising a plurality of possible values in a range between 0 and 1; and the one or more non-transitory computer-readable media which store the instructions. 20. A method of testing an apparatus, comprising: testing whether the apparatus has at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs deep learning training comprising the steps of: receiving candidate units for classification; and classifying the candidate units by soft labelling, wherein soft labelling provides at least one label comprising a plurality of possible values in a range between 0 and 1; and testing whether the apparatus has the one or more non-transitory computer-readable media which store the instructions.