Inference Graphs for CNN Interpretation

• URL: http://arxiv.org/abs/2110.10568v1
• Date: Wed, 20 Oct 2021 13:56:09 GMT
• Title: Inference Graphs for CNN Interpretation
• Authors: Yael Konforti, Alon Shpigler, Boaz Lernerand Aharon Bar-Hillel
• Abstract summary: Convolutional neural networks (CNNs) have achieved superior accuracy in many visual related tasks. We propose to model the network hidden layers activity using probabilistic models. We show that such graphs are useful for understanding the general inference process of a class, as well as explaining decisions the network makes regarding specific images.
• Score: 12.765543440576144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural networks (CNNs) have achieved superior accuracy in many visual related tasks. However, the inference process through intermediate layers is opaque, making it difficult to interpret such networks or develop trust in their operation. We propose to model the network hidden layers activity using probabilistic models. The activity patterns in layers of interest are modeled as Gaussian mixture models, and transition probabilities between clusters in consecutive modeled layers are estimated. Based on maximum-likelihood considerations, nodes and paths relevant for network prediction are chosen, connected, and visualized as an inference graph. We show that such graphs are useful for understanding the general inference process of a class, as well as explaining decisions the network makes regarding specific images.

Related papers

• Learning local discrete features in explainable-by-design convolutional neural networks [0.0]
  We introduce an explainable-by-design convolutional neural network (CNN) based on the lateral inhibition mechanism. The model consists of the predictor, that is a high-accuracy CNN with residual or dense skip connections. By collecting observations and directly calculating probabilities, we can explain causal relationships between motifs of adjacent levels.
  arXiv  Detail & Related papers  (2024-10-31T18:39:41Z)
• GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection [51.608147732998994]
  Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. We propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features.
  arXiv  Detail & Related papers  (2024-01-17T13:04:23Z)
• Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification [0.0]
  We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements.
  arXiv  Detail & Related papers  (2023-07-24T13:39:21Z)
• Decomposing neural networks as mappings of correlation functions [57.52754806616669]
  We study the mapping between probability distributions implemented by a deep feed-forward network. We identify essential statistics in the data, as well as different information representations that can be used by neural networks.
  arXiv  Detail & Related papers  (2022-02-10T09:30:31Z)
• Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks [0.6236890292833384]
  In this paper, we propose a novel algorithm called Bayesian Graph Convolutional Network using Neighborhood Random Walk Sampling (BGCN-NRWS) BGCN-NRWS uses a Markov Chain Monte Carlo (MCMC) based graph sampling algorithm utilizing graph structure, reduces overfitting by using a variational inference layer, and yields consistently competitive classification results compared to the state-of-the-art in semi-supervised node classification.
  arXiv  Detail & Related papers  (2021-12-14T20:58:27Z)
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations [54.05212871508062]
  We propose a novel approach for dynamic network representation learning based on Temporal Graph Network. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
  arXiv  Detail & Related papers  (2021-08-19T15:39:52Z)
• Graph Belief Propagation Networks [34.137798598227874]
  We introduce a model that combines the advantages of graph neural networks and collective classification. In our model, potentials on each node only depend on that node's features, and edge potentials are learned via a coupling matrix. Our approach can be viewed as either an interpretable message-passing graph neural network or a collective classification method with higher capacity and modernized training.
  arXiv  Detail & Related papers  (2021-06-06T05:24:06Z)
• Mutually exciting point process graphs for modelling dynamic networks [0.0]
  A new class of models for dynamic networks is proposed, called mutually exciting point process graphs (MEG) MEG is a scalable network-wide statistical model for point processes with dyadic marks, which can be used for anomaly detection. The model is tested on simulated graphs and real world computer network datasets, demonstrating excellent performance.
  arXiv  Detail & Related papers  (2021-02-11T10:14:55Z)
• Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction [158.88345945211185]
  We present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect, i.e. structured multi-scale features learning and fusion. We propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.
  arXiv  Detail & Related papers  (2021-01-08T04:14:29Z)
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
  This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
  arXiv  Detail & Related papers  (2020-12-09T12:40:13Z)
• Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking [63.49779304362376]
  Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. We introduce a post-hoc method for interpreting the predictions of GNNs which identifies unnecessary edges. We show that we can drop a large proportion of edges without deteriorating the performance of the model.
  arXiv  Detail & Related papers  (2020-10-01T17:51:19Z)

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.