Diversified Dynamic Routing for Vision Tasks
This addresses efficiency challenges in vision models for researchers and practitioners, though it is incremental as it builds on dynamic routing methods.
The paper tackles the problem of efficiently assigning specialized experts in mixture-of-experts models for vision tasks by proposing Diversified Dynamic Routing (DivDR), which explicitly learns to partition data and assign experts unsupervised, resulting in improved performance on Cityscapes semantic segmentation and MS-COCO object detection and instance segmentation.
Deep learning models for vision tasks are trained on large datasets under the assumption that there exists a universal representation that can be used to make predictions for all samples. Whereas high complexity models are proven to be capable of learning such representations, a mixture of experts trained on specific subsets of the data can infer the labels more efficiently. However using mixture of experts poses two new problems, namely (i) assigning the correct expert at inference time when a new unseen sample is presented. (ii) Finding the optimal partitioning of the training data, such that the experts rely the least on common features. In Dynamic Routing (DR) a novel architecture is proposed where each layer is composed of a set of experts, however without addressing the two challenges we demonstrate that the model reverts to using the same subset of experts. In our method, Diversified Dynamic Routing (DivDR) the model is explicitly trained to solve the challenge of finding relevant partitioning of the data and assigning the correct experts in an unsupervised approach. We conduct several experiments on semantic segmentation on Cityscapes and object detection and instance segmentation on MS-COCO showing improved performance over several baselines.