Refining Neural Networks with Compositional Explanations

• URL: http://arxiv.org/abs/2103.10415v1
• Date: Thu, 18 Mar 2021 17:48:54 GMT
• Title: Refining Neural Networks with Compositional Explanations
• Authors: Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, Xiang Ren
• Abstract summary: We propose to refine a learned model by collecting human-provided compositional explanations on the models' failure cases. We demonstrate the effectiveness of the proposed approach on two text classification tasks.
• Score: 31.84868477264624
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Neural networks are prone to learning spurious correlations from biased datasets, and are thus vulnerable when making inferences in a new target domain. Prior work reveals spurious patterns via post-hoc model explanations which compute the importance of input features, and further eliminates the unintended model behaviors by regularizing importance scores with human knowledge. However, such regularization technique lacks flexibility and coverage, since only importance scores towards a pre-defined list of features are adjusted, while more complex human knowledge such as feature interaction and pattern generalization can hardly be incorporated. In this work, we propose to refine a learned model by collecting human-provided compositional explanations on the models' failure cases. By describing generalizable rules about spurious patterns in the explanation, more training examples can be matched and regularized, tackling the challenge of regularization coverage. We additionally introduce a regularization term for feature interaction to support more complex human rationale in refining the model. We demonstrate the effectiveness of the proposed approach on two text classification tasks by showing improved performance in target domain after refinement.

Related papers

• Improving Network Interpretability via Explanation Consistency Evaluation [56.14036428778861]
  We propose a framework that acquires more explainable activation heatmaps and simultaneously increase the model performance. Specifically, our framework introduces a new metric, i.e., explanation consistency, to reweight the training samples adaptively in model learning. Our framework then promotes the model learning by paying closer attention to those training samples with a high difference in explanations.
  arXiv  Detail & Related papers  (2024-08-08T17:20:08Z)
• Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability [70.60433013657693]
  Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level. We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
  arXiv  Detail & Related papers  (2023-06-14T23:24:01Z)
• Regularization Through Simultaneous Learning: A Case Study on Plant Classification [0.0]
  This paper introduces Simultaneous Learning, a regularization approach drawing on principles of Transfer Learning and Multi-task Learning. We leverage auxiliary datasets with the target dataset, the UFOP-HVD, to facilitate simultaneous classification guided by a customized loss function. Remarkably, our approach demonstrates superior performance over models without regularization.
  arXiv  Detail & Related papers  (2023-05-22T19:44:57Z)
• Robust Graph Representation Learning via Predictive Coding [46.22695915912123]
  Predictive coding is a message-passing framework initially developed to model information processing in the brain. In this work, we build models that rely on the message-passing rule of predictive coding. We show that the proposed models are comparable to standard ones in terms of performance in both inductive and transductive tasks.
  arXiv  Detail & Related papers  (2022-12-09T03:58:22Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Explaining, Evaluating and Enhancing Neural Networks' Learned Representations [2.1485350418225244]
  We show how explainability can be an aid, rather than an obstacle, towards better and more efficient representations. We employ such attributions to define two novel scores for evaluating the informativeness and the disentanglement of latent embeddings. We show that adopting our proposed scores as constraints during the training of a representation learning task improves the downstream performance of the model.
  arXiv  Detail & Related papers  (2022-02-18T19:00:01Z)
• Generalizable Information Theoretic Causal Representation [37.54158138447033]
  We propose to learn causal representation from observational data by regularizing the learning procedure with mutual information measures according to our hypothetical causal graph. The optimization involves a counterfactual loss, based on which we deduce a theoretical guarantee that the causality-inspired learning is with reduced sample complexity and better generalization ability.
  arXiv  Detail & Related papers  (2022-02-17T00:38:35Z)
• Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach [80.8446673089281]
  We propose a new learning paradigm with graph representation and learning. Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
  arXiv  Detail & Related papers  (2021-10-09T09:02:45Z)
• Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates [26.527311287924995]
  influence tuning can help deconfounding the model from spurious patterns in data. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data.
  arXiv  Detail & Related papers  (2021-10-07T06:59:46Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision [57.14468881854616]
  We propose an auxiliary training objective that improves the generalization capabilities of neural networks. We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task. Models trained with this technique demonstrate improved performance on out-of-distribution test sets.
  arXiv  Detail & Related papers  (2020-04-20T02:47:49Z)

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.