Abstract: Methods and apparatuses for compression of feature tensors of a neural network are provided. One or more encoding parameters for encoding the channels of a feature tensor are selected according to the importance of the channels. This enables unequal bit allocation according to the importance. Furthermore, the deployed neural network may be trained or fine-tuned considering the effect of encoding noise applied to the intermediate feature tensors. According to the present disclosure, the encoding and modified training methods are advantageous at least for employment in a collaborative intelligence framework.

Abstract: The present disclosure relates to methods and apparatuses for modifying a quantizer. In particular, within a preliminary set of quantization levels, at least one quantization level is modified based on optimization involving distortion for a predetermined set of input values. At least one another quantization level out of the preliminary set is not modified. The not modified (non-modifiable) quantization level is the minimum clipping value or the maximum clipping value. The modification may facilitate increasing the dynamic range of the quantized/inverse-quantized data. Such modified quantizer may be advantageous for employment in neural networks to compress their data such as feature maps or the like. It may improve accuracy of the neural network.

Abstract: The present disclosure relates to scalable encoding and decoding of pictures. In particular, a picture is processed by one or more network layers of a trained module to obtain base layer features. Then, enhancement layer features are obtained, e.g. by a trained network processing in sample domain. The base layer features are for use in computer vision processing. The base layer features together with enhancement layer features are for use in picture reconstruction, e.g. for human vision. The base layer features and the enhancement layer features are coded in a respective base layer bitstream and an enhancement layer bitstream. Accordingly, a scalable coding is provided which supports computer vision processing and/or picture reconstruction.