Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability

• URL: http://arxiv.org/abs/2204.10598v3
• Date: Thu, 27 Apr 2023 07:02:25 GMT
• Title: Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability
• Authors: Svetlana Pavlitska, Christian Hubschneider, Lukas Struppek and J. Marius Z\"ollner
• Abstract summary: Sparsely-gated Mixture of Expert (MoE) layers have been successfully applied for scaling large transformers. In this work, we apply sparse MoE layers to CNNs for computer vision tasks and analyze the resulting effect on model interpretability.
• Score: 3.021134753248103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparsely-gated Mixture of Expert (MoE) layers have been recently successfully applied for scaling large transformers, especially for language modeling tasks. An intriguing side effect of sparse MoE layers is that they convey inherent interpretability to a model via natural expert specialization. In this work, we apply sparse MoE layers to CNNs for computer vision tasks and analyze the resulting effect on model interpretability. To stabilize MoE training, we present both soft and hard constraint-based approaches. With hard constraints, the weights of certain experts are allowed to become zero, while soft constraints balance the contribution of experts with an additional auxiliary loss. As a result, soft constraints handle expert utilization better and support the expert specialization process, while hard constraints maintain more generalized experts and increase overall model performance. Our findings demonstrate that experts can implicitly focus on individual sub-domains of the input space. For example, experts trained for CIFAR-100 image classification specialize in recognizing different domains such as flowers or animals without previous data clustering. Experiments with RetinaNet and the COCO dataset further indicate that object detection experts can also specialize in detecting objects of distinct sizes.

Related papers

• Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection [63.96018203905272]
  We propose to reduce the sampling cost by pruning a pretrained diffusion model into a mixture of efficient experts. We demonstrate the effectiveness of our method, DiffPruning, across several datasets.
  arXiv  Detail & Related papers  (2024-09-23T21:27:26Z)
• Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts [44.09546603624385]
  We introduce a notion of expert specialization for Soft MoE. We show that when there are many small experts, the architecture is implicitly biased in a fashion that allows us to efficiently approximate the specialized expert subset.
  arXiv  Detail & Related papers  (2024-09-02T00:39:00Z)
• Generalization Error Analysis for Sparse Mixture-of-Experts: A Preliminary Study [65.11303133775857]
  Mixture-of-Experts (MoE) computation amalgamates predictions from several specialized sub-models (referred to as experts) Sparse MoE selectively engages only a limited number, or even just one expert, significantly reducing overhead while empirically preserving, and sometimes even enhancing, performance.
  arXiv  Detail & Related papers  (2024-03-26T05:48:02Z)
• Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization [51.98792406392873]
  Mixture of Experts (MoE) provides a powerful way to decompose dense layers into smaller, modular computations. A major challenge lies in the computational cost of scaling the number of experts high enough to achieve fine-grained specialization. We propose the Multilinear Mixture of Experts ($mu$MoE) layer to address this, focusing on vision models.
  arXiv  Detail & Related papers  (2024-02-19T21:20:22Z)
• On Least Square Estimation in Softmax Gating Mixture of Experts [78.3687645289918]
  We investigate the performance of the least squares estimators (LSE) under a deterministic MoE model. We establish a condition called strong identifiability to characterize the convergence behavior of various types of expert functions. Our findings have important practical implications for expert selection.
  arXiv  Detail & Related papers  (2024-02-05T12:31:18Z)
• Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy [84.11508381847929]
  Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks. We propose M-SMoE, which leverages routing statistics to guide expert merging. Our MC-SMoE achieves up to 80% memory and a 20% FLOPs reduction, with virtually no loss in performance.
  arXiv  Detail & Related papers  (2023-10-02T16:51:32Z)
• Spatial Mixture-of-Experts [16.71096722340687]
  We introduce the Spatial Mixture-of-Experts layer, which learns spatial structure in the input domain and routes experts at a fine-grained level to utilize it. We show strong results for SMoEs on numerous tasks, and set new results for medium-range weather prediction and post-processing ensemble weather forecasts.
  arXiv  Detail & Related papers  (2022-11-24T09:31:02Z)
• MoEC: Mixture of Expert Clusters [93.63738535295866]
  Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation.
  arXiv  Detail & Related papers  (2022-07-19T06:09:55Z)

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.