FICNN: A Framework for the Interpretation of Deep Convolutional Neural Networks

• URL: http://arxiv.org/abs/2305.10121v1
• Date: Wed, 17 May 2023 10:59:55 GMT
• Title: FICNN: A Framework for the Interpretation of Deep Convolutional Neural Networks
• Authors: Hamed Behzadi-Khormouji and Jos\'e Oramas
• Abstract summary: The aim of this paper is to propose a framework for the study of interpretation methods designed for CNN models trained from visual data. Our framework highlights that just a very small amount of the suggested factors, and combinations thereof, have been actually studied.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the continue development of Convolutional Neural Networks (CNNs), there is a growing concern regarding representations that they encode internally. Analyzing these internal representations is referred to as model interpretation. While the task of model explanation, justifying the predictions of such models, has been studied extensively; the task of model interpretation has received less attention. The aim of this paper is to propose a framework for the study of interpretation methods designed for CNN models trained from visual data. More specifically, we first specify the difference between the interpretation and explanation tasks which are often considered the same in the literature. Then, we define a set of six specific factors that can be used to characterize interpretation methods. Third, based on the previous factors, we propose a framework for the positioning of interpretation methods. Our framework highlights that just a very small amount of the suggested factors, and combinations thereof, have been actually studied. Consequently, leaving significant areas unexplored. Following the proposed framework, we discuss existing interpretation methods and give some attention to the evaluation protocols followed to validate them. Finally, the paper highlights capabilities of the methods in producing feedback for enabling interpretation and proposes possible research problems arising from the framework.

Related papers

• Deep Model Interpretation with Limited Data : A Coreset-based Approach [0.810304644344495]
  We propose a coreset-based interpretation framework that utilizes coreset selection methods to sample a representative subset of the large dataset for the interpretation task. We propose a similarity-based evaluation protocol to assess the robustness of model interpretation methods towards the amount data they take as input.
  arXiv  Detail & Related papers  (2024-10-01T09:07:24Z)
• Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors [3.4253416336476246]
  We focus on the analysis of counterfactual, contrastive explanations. We propose a new back translation-inspired evaluation methodology. We show that by iteratively feeding the counterfactual to the explainer we can obtain valuable insights into the behaviour of both the predictor and the explainer models.
  arXiv  Detail & Related papers  (2023-05-26T16:04:28Z)
• On the Faithfulness Measurements for Model Interpretations [100.2730234575114]
  Post-hoc interpretations aim to uncover how natural language processing (NLP) models make predictions. To tackle these issues, we start with three criteria: the removal-based criterion, the sensitivity of interpretations, and the stability of interpretations. Motivated by the desideratum of these faithfulness notions, we introduce a new class of interpretation methods that adopt techniques from the adversarial domain.
  arXiv  Detail & Related papers  (2021-04-18T09:19:44Z)
• Local Interpretations for Explainable Natural Language Processing: A Survey [5.717407321642629]
  This work investigates various methods to improve the interpretability of deep neural networks for Natural Language Processing (NLP) tasks. We provide a comprehensive discussion on the definition of the term interpretability and its various aspects at the beginning of this work.
  arXiv  Detail & Related papers  (2021-03-20T02:28:33Z)
• Interpretable Deep Learning: Interpretations, Interpretability, Trustworthiness, and Beyond [49.93153180169685]
  We introduce and clarify two basic concepts-interpretations and interpretability-that people usually get confused. We elaborate the design of several recent interpretation algorithms, from different perspectives, through proposing a new taxonomy. We summarize the existing work in evaluating models' interpretability using "trustworthy" interpretation algorithms.
  arXiv  Detail & Related papers  (2021-03-19T08:40:30Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Towards Interpretable Reasoning over Paragraph Effects in Situation [126.65672196760345]
  We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect. We propose a sequential approach for this task which explicitly models each step of the reasoning process with neural network modules. In particular, five reasoning modules are designed and learned in an end-to-end manner, which leads to a more interpretable model.
  arXiv  Detail & Related papers  (2020-10-03T04:03:52Z)
• Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
  We establish a novel set of evaluation criteria for such feature based explanations by analysis. We obtain new explanations that are loosely necessary and sufficient for a prediction. We extend the explanation to extract the set of features that would move the current prediction to a target class.
  arXiv  Detail & Related papers  (2020-05-31T05:52:05Z)
• Obtaining Faithful Interpretations from Compositional Neural Networks [72.41100663462191]
  We evaluate the intermediate outputs of NMNs on NLVR2 and DROP datasets. We find that the intermediate outputs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour.
  arXiv  Detail & Related papers  (2020-05-02T06:50:35Z)

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.