  We investigate latent-space scalability for multi-task collaborative
intelligence, where one of the tasks is object detection and the other is input
reconstruction. In our proposed approach, part of the latent space can be
selectively decoded to support object detection while the remainder can be
decoded when input reconstruction is needed. Such an approach allows reduced
computational resources when only object detection is required, and this can be
achieved without reconstructing input pixels. By varying the scaling factors of
various terms in the training loss function, the system can be trained to
achieve various trade-offs between object detection accuracy and input
reconstruction quality. Experiments are conducted to demonstrate the adjustable
system performance on the two tasks compared to the relevant benchmarks.
