Routing Towards Discriminative Power of Class Capsules

• URL: http://arxiv.org/abs/2103.04278v1
• Date: Sun, 7 Mar 2021 05:49:38 GMT
• Title: Routing Towards Discriminative Power of Class Capsules
• Authors: Haoyu Yang, Shuhe Li, Bei Yu
• Abstract summary: We propose a routing algorithm that incorporates a regularized quadratic programming problem which can be solved efficiently. We conduct experiments on MNIST, MNIST-Fashion, and CIFAR-10 and show competitive classification results compared to existing capsule networks.
• Score: 7.347145775695176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Capsule networks are recently proposed as an alternative to modern neural network architectures. Neurons are replaced with capsule units that represent specific features or entities with normalized vectors or matrices. The activation of lower layer capsules affects the behavior of the following capsules via routing links that are constructed during training via certain routing algorithms. We discuss the routing-by-agreement scheme in dynamic routing algorithm which, in certain cases, leads the networks away from optimality. To obtain better and faster convergence, we propose a routing algorithm that incorporates a regularized quadratic programming problem which can be solved efficiently. Particularly, the proposed routing algorithm targets directly on the discriminative power of class capsules making the correct decision on input instances. We conduct experiments on MNIST, MNIST-Fashion, and CIFAR-10 and show competitive classification results compared to existing capsule networks.

Related papers

• Learning the Optimal Path and DNN Partition for Collaborative Edge Inference [4.368333109035076]
  Deep Neural Networks (DNNs) have catalyzed the development of numerous intelligent mobile applications and services. To address this, collaborative edge inference has been proposed. This method involves partitioning a DNN inference task into several subtasks and distributing these across multiple network nodes. We introduce a new bandit algorithm, B-EXPUCB, which combines elements of the classical blocked EXP3 and LinUCB algorithms, and demonstrate its sublinear regret.
  arXiv  Detail & Related papers  (2024-10-02T01:12:16Z)
• Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry [0.3848364262836075]
  It is shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.
  arXiv  Detail & Related papers  (2024-06-18T13:32:12Z)
• Concurrent Training and Layer Pruning of Deep Neural Networks [0.0]
  We propose an algorithm capable of identifying and eliminating irrelevant layers of a neural network during the early stages of training. We employ a structure using residual connections around nonlinear network sections that allow the flow of information through the network once a nonlinear section is pruned.
  arXiv  Detail & Related papers  (2024-06-06T23:19:57Z)
• An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem [67.92544792239086]
  We introduce an NN-based solver to significantly narrow the gap with advanced metaheuristics. First, we propose direction-aware facilitating attention model (DaAM) to incorporate directionality into the embedding process. Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy.
  arXiv  Detail & Related papers  (2024-03-11T02:17:42Z)
• Robust Stochastically-Descending Unrolled Networks [85.6993263983062]
  Deep unrolling is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. We show that convergence guarantees and generalizability of the unrolled networks are still open theoretical problems. We numerically assess unrolled architectures trained under the proposed constraints in two different applications.
  arXiv  Detail & Related papers  (2023-12-25T18:51:23Z)
• ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method [6.028175460199198]
  We introduce a novel, non-iterative routing mechanism for Capsule Networks. We harness a shared Capsule subspace, negating the need to project each lower-level Capsule to each higher-level Capsule. Our findings underscore the potential of our proposed methodology in enhancing the operational efficiency and performance of Capsule Networks.
  arXiv  Detail & Related papers  (2023-07-19T12:39:40Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Relational Deep Reinforcement Learning for Routing in Wireless Networks [2.997420836766863]
  We develop a distributed routing strategy based on deep reinforcement learning that generalizes to diverse traffic patterns, congestion levels, network connectivity, and link dynamics. Our algorithm outperforms shortest path and backpressure routing with respect to packets delivered and delay per packet.
  arXiv  Detail & Related papers  (2020-12-31T16:28:21Z)
• Purification and Entanglement Routing on Quantum Networks [55.41644538483948]
  A quantum network equipped with imperfect channel fidelities and limited memory storage time can distribute entanglement between users. We introduce effectives enabling fast path-finding algorithms for maximizing entanglement shared between two nodes on a quantum network.
  arXiv  Detail & Related papers  (2020-11-23T19:00:01Z)
• Wasserstein Routed Capsule Networks [90.16542156512405]
  We propose a new parameter efficient capsule architecture, that is able to tackle complex tasks. We show that our network is able to substantially outperform other capsule approaches by over 1.2 % on CIFAR-10.
  arXiv  Detail & Related papers  (2020-07-22T14:38:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.