Understanding Multimodal Deep Neural Networks: A Concept Selection View

• URL: http://arxiv.org/abs/2404.08964v1
• Date: Sat, 13 Apr 2024 11:06:49 GMT
• Title: Understanding Multimodal Deep Neural Networks: A Concept Selection View
• Authors: Chenming Shang, Hengyuan Zhang, Hao Wen, Yujiu Yang,
• Abstract summary: Concept-based models map the black-box visual representations extracted by deep neural networks onto a set of human-understandable concepts. We propose a two-stage Concept Selection Model (CSM) to mine core concepts without introducing any human priors. Our approach achieves comparable performance to end-to-end black-box models.
• Score: 29.08342307127578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The multimodal deep neural networks, represented by CLIP, have generated rich downstream applications owing to their excellent performance, thus making understanding the decision-making process of CLIP an essential research topic. Due to the complex structure and the massive pre-training data, it is often regarded as a black-box model that is too difficult to understand and interpret. Concept-based models map the black-box visual representations extracted by deep neural networks onto a set of human-understandable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. However, these methods involve the datasets labeled with fine-grained attributes by expert knowledge, which incur high costs and introduce excessive human prior knowledge and bias. In this paper, we observe the long-tail distribution of concepts, based on which we propose a two-stage Concept Selection Model (CSM) to mine core concepts without introducing any human priors. The concept greedy rough selection algorithm is applied to extract head concepts, and then the concept mask fine selection method performs the extraction of core concepts. Experiments show that our approach achieves comparable performance to end-to-end black-box models, and human evaluation demonstrates that the concepts discovered by our method are interpretable and comprehensible for humans.

Related papers

• Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery [52.498055901649025]
  Concept Bottleneck Models (CBMs) have been proposed to address the 'black-box' problem of deep neural networks. We propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model.
  arXiv  Detail & Related papers  (2024-07-19T17:50:11Z)
• Restyling Unsupervised Concept Based Interpretable Networks with Generative Models [14.604305230535026]
  We propose a novel method that relies on mapping the concept features to the latent space of a pretrained generative model. We quantitatively ascertain the efficacy of our method in terms of accuracy of the interpretable prediction network, fidelity of reconstruction, as well as faithfulness and consistency of learnt concepts.
  arXiv  Detail & Related papers  (2024-07-01T14:39:41Z)
• Incremental Residual Concept Bottleneck Models [29.388549499546556]
  Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts. We propose the Incremental Residual Concept Bottleneck Model (Res-CBM) to address the challenge of concept completeness. Our approach can be applied to any user-defined concept bank, as a post-hoc processing method to enhance the performance of any CBMs.
  arXiv  Detail & Related papers  (2024-04-13T12:02:19Z)
• A survey on Concept-based Approaches For Model Improvement [2.1516043775965565]
  Concepts are known to be the thinking ground of humans. We provide a systematic review and taxonomy of various concept representations and their discovery algorithms in Deep Neural Networks (DNNs) We also provide details on concept-based model improvement literature marking the first comprehensive survey of these methods.
  arXiv  Detail & Related papers  (2024-03-21T17:09:20Z)
• Separable Multi-Concept Erasure from Diffusion Models [52.51972530398691]
  We propose a Separable Multi-concept Eraser (SepME) to eliminate unsafe concepts from large-scale diffusion models. The latter separates optimizable model weights, making each weight increment correspond to a specific concept erasure. Extensive experiments indicate the efficacy of our approach in eliminating concepts, preserving model performance, and offering flexibility in the erasure or recovery of various concepts.
  arXiv  Detail & Related papers  (2024-02-03T11:10:57Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)
• Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models [0.0]
  We present an extension to the method of concept detection, named emphconcept backpropagation, which provides a way of analysing how the information representing a given concept is internalised in a given neural network model.
  arXiv  Detail & Related papers  (2023-07-24T08:21:13Z)
• Automatic Concept Extraction for Concept Bottleneck-based Video Classification [58.11884357803544]
  We present an automatic Concept Discovery and Extraction module that rigorously composes a necessary and sufficient set of concept abstractions for concept-based video classification. Our method elicits inherent complex concept abstractions in natural language to generalize concept-bottleneck methods to complex tasks.
  arXiv  Detail & Related papers  (2022-06-21T06:22:35Z)
• Human-Centered Concept Explanations for Neural Networks [47.71169918421306]
  We introduce concept explanations including the class of Concept Activation Vectors (CAV) We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.
  arXiv  Detail & Related papers  (2022-02-25T01:27:31Z)
• MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks [10.06397994266945]
  We propose MACE: a Model Agnostic Concept Extractor, which can explain the working of a convolutional network through smaller concepts. We validate our framework using VGG16 and ResNet50 CNN architectures, and on datasets like Animals With Attributes 2 (AWA2) and Places365.
  arXiv  Detail & Related papers  (2020-11-03T04:40:49Z)
• A Competence-aware Curriculum for Visual Concepts Learning via Question Answering [95.35905804211698]
  We propose a competence-aware curriculum for visual concept learning in a question-answering manner. We design a neural-symbolic concept learner for learning the visual concepts and a multi-dimensional Item Response Theory (mIRT) model for guiding the learning process. Experimental results on CLEVR show that with a competence-aware curriculum, the proposed method achieves state-of-the-art performances.
  arXiv  Detail & Related papers  (2020-07-03T05:08:09Z)

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.