Inducing Semantic Grouping of Latent Concepts for Explanations: An Ante-Hoc Approach

• URL: http://arxiv.org/abs/2108.11761v1
• Date: Wed, 25 Aug 2021 07:09:57 GMT
• Title: Inducing Semantic Grouping of Latent Concepts for Explanations: An Ante-Hoc Approach
• Authors: Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar, Vineeth N Balasubramanian
• Abstract summary: We show that by exploiting latent and properly modifying different parts of the model can result better explanation as well as provide superior predictive performance. We also proposed a technique of using two different self-supervision techniques to extract meaningful concepts related to the type of self-supervision considered.
• Score: 18.170504027784183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-explainable deep models are devised to represent the hidden concepts in the dataset without requiring any posthoc explanation generation technique. We worked with one of such models motivated by explicitly representing the classifier function as a linear function and showed that by exploiting probabilistic latent and properly modifying different parts of the model can result better explanation as well as provide superior predictive performance. Apart from standard visualization techniques, we proposed a new technique which can strengthen human understanding towards hidden concepts. We also proposed a technique of using two different self-supervision techniques to extract meaningful concepts related to the type of self-supervision considered and achieved significant performance boost. The most important aspect of our method is that it works nicely in a low data regime and reaches the desired accuracy in a few number of epochs. We reported exhaustive results with CIFAR10, CIFAR100, and AWA2 datasets to show effect of our method with moderate and relatively complex datasets.

Related papers

• High-Performance Few-Shot Segmentation with Foundation Models: An Empirical Study [64.06777376676513]
  We develop a few-shot segmentation (FSS) framework based on foundation models. To be specific, we propose a simple approach to extract implicit knowledge from foundation models to construct coarse correspondence. Experiments on two widely used datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-09-10T08:04:11Z)
• Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales [3.242050660144211]
  Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. We present a methodology for incorporating rationales, which are text annotations explaining human decisions, into text classification models.
  arXiv  Detail & Related papers  (2024-04-03T22:39:33Z)
• Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction [0.0]
  We introduce a three-stage process: obtaining SHAP values to explain model predictions, clustering SHAP values to identify distinct patterns and outliers, and refining the model based on the derived SHAP clustering characteristics. Our experiments demonstrate the effectiveness of our approach in both task types, resulting in improved predictive performance and interpretable model explanations.
  arXiv  Detail & Related papers  (2024-02-07T15:58:51Z)
• Harnessing Diffusion Models for Visual Perception with Meta Prompts [68.78938846041767]
  We propose a simple yet effective scheme to harness a diffusion model for visual perception tasks. We introduce learnable embeddings (meta prompts) to the pre-trained diffusion models to extract proper features for perception. Our approach achieves new performance records in depth estimation tasks on NYU depth V2 and KITTI, and in semantic segmentation task on CityScapes.
  arXiv  Detail & Related papers  (2023-12-22T14:40:55Z)
• Benchmarking and Enhancing Disentanglement in Concept-Residual Models [4.177318966048984]
  Concept bottleneck models (CBMs) are interpretable models that first predict a set of semantically meaningful features. CBMs' performance depends on the engineered features and can severely suffer from incomplete sets of concepts. This work proposes three novel approaches to mitigate information leakage by disentangling concepts and residuals.
  arXiv  Detail & Related papers  (2023-11-30T21:07:26Z)
• Towards interpretable-by-design deep learning algorithms [11.154826546951414]
  A proposed framework named I recasts the standard supervised classification problem into a function of similarity to a set of prototypes derived from the training data. We show that one can turn such DL models into conceptually simpler, explainable-through-prototypes ones.
  arXiv  Detail & Related papers  (2023-11-19T18:40:49Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• From Canonical Correlation Analysis to Self-supervised Graph Neural Networks [99.44881722969046]
  We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data. We optimize an innovative feature-level objective inspired by classical Canonical Correlation Analysis. Our method performs competitively on seven public graph datasets.
  arXiv  Detail & Related papers  (2021-06-23T15:55:47Z)
• A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
  We develop a list of diagnostic properties for evaluating existing explainability techniques. We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
  arXiv  Detail & Related papers  (2020-09-25T12:01:53Z)
• Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning [21.89909688056478]
  We propose a new two-level joint idea to augment the generative network with an inference network during training. This provides strong cross-modal interaction for effective transfer of knowledge between visual and semantic domains. We evaluate our approach on four benchmark datasets against several state-of-the-art methods, and show its performance.
  arXiv  Detail & Related papers  (2020-07-15T15:34:09Z)

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.