Conference item : Poster
Diversified dynamic routing for vision tasks
- Abstract:
- 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) [21] 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 2.4MB, Terms of use)
-
- Publisher copy:
- 10.1007/978-3-031-25069-9_48
Authors
- Publisher:
- Springer
- Host title:
- Computer Vision – ECCV 2022 Workshops
- Pages:
- 756-772
- Series:
- Lecture Notes in Computer Science
- Series number:
- 13804
- Place of publication:
- Cham, Switzerland
- Publication date:
- 2023-02-14
- Acceptance date:
- 2022-06-19
- Event title:
- 3rd Visual Inductive Priors for Data-Efficient Deep Learning Workshop (VIPriors 2022)
- Event location:
- Tel Aviv, Israel
- Event website:
- https://vipriors.github.io/2022/
- Event start date:
- 2022-10-24
- Event end date:
- 2022-10-24
- DOI:
- EISSN:
-
1611-3349
- ISSN:
-
0302-9743
- EISBN:
- 9783031250699
- ISBN:
- 9783031250682
- Language:
-
English
- Keywords:
- Subtype:
-
Poster
- Pubs id:
-
1304014
- Local pid:
-
pubs:1304014
- Deposit date:
-
2022-11-14
- ARK identifier:
Terms of use
- Copyright holder:
- Csaba et al.
- Copyright date:
- 2023
- Rights statement:
- © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Notes:
- This is the accepted manuscript version of the paper. The final version is available online from Springer at: https://doi.org/10.1007/978-3-031-25069-9_48
If you are the owner of this record, you can report an update to it here: Report update to this record