Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification

• URL: http://arxiv.org/abs/2405.03301v1
• Date: Mon, 6 May 2024 09:21:35 GMT
• Title: Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification
• Authors: Matteo Bianchi, Antonio De Santis, Andrea Tocchetti, Marco Brambilla,
• Abstract summary: State-of-the-art explainability methods generate saliency maps to show where a specific class is identified. We introduce a post-hoc method that explains the entire feature extraction process of a Convolutional Neural Network. We also show an approach to generate global explanations by aggregating labels across multiple images.
• Score: 5.087579454836169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors. State-of-the-art explainability methods generate saliency maps to show where a specific class is identified, without providing a detailed explanation of the model's decision process. Striving to address such a need, we introduce a post-hoc method that explains the entire feature extraction process of a Convolutional Neural Network. These explanations include a layer-wise representation of the features the model extracts from the input. Such features are represented as saliency maps generated by clustering and merging similar feature maps, to which we associate a weight derived by generalizing Grad-CAM for the proposed methodology. To further enhance these explanations, we include a set of textual labels collected through a gamified crowdsourcing activity and processed using NLP techniques and Sentence-BERT. Finally, we show an approach to generate global explanations by aggregating labels across multiple images.

Related papers

• Accurate Explanation Model for Image Classifiers using Class Association Embedding [5.378105759529487]
  We propose a generative explanation model that combines the advantages of global and local knowledge. Class association embedding (CAE) encodes each sample into a pair of separated class-associated and individual codes. Building-block coherency feature extraction algorithm is proposed that efficiently separates class-associated features from individual ones.
  arXiv  Detail & Related papers  (2024-06-12T07:41:00Z)
• Neural Clustering based Visual Representation Learning [61.72646814537163]
  Clustering is one of the most classic approaches in machine learning and data analysis. We propose feature extraction with clustering (FEC), which views feature extraction as a process of selecting representatives from data. FEC alternates between grouping pixels into individual clusters to abstract representatives and updating the deep features of pixels with current representatives.
  arXiv  Detail & Related papers  (2024-03-26T06:04:50Z)
• EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models [52.3015009878545]
  We develop an image segmentor capable of generating fine-grained segmentation maps without any additional training. Our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps. In extensive experiments, the produced segmentation maps are demonstrated to be well delineated and capture detailed parts of the images.
  arXiv  Detail & Related papers  (2024-01-22T07:34:06Z)
• Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification [17.373054348176932]
  In this work, a post-hoc interpretation tool named feature activation map (FAM) is proposed. FAM can interpret deep learning models without FC layers as a classifier. Experiments conducted on ten deep learning models for few-shot image classification, contrastive learning image classification and image retrieval tasks demonstrate the effectiveness of the proposed FAM algorithm.
  arXiv  Detail & Related papers  (2023-07-11T05:33:46Z)
• Text Descriptions are Compressive and Invariant Representations for Visual Learning [63.3464863723631]
  We show that an alternative approach, in line with humans' understanding of multiple visual features per class, can provide compelling performance in the robust few-shot learning setting. In particular, we introduce a novel method, textit SLR-AVD (Sparse Logistic Regression using Augmented Visual Descriptors). This method first automatically generates multiple visual descriptions of each class via a large language model (LLM), then uses a VLM to translate these descriptions to a set of visual feature embeddings of each image, and finally uses sparse logistic regression to select a relevant subset of these features to classify
  arXiv  Detail & Related papers  (2023-07-10T03:06:45Z)
• Rethinking interpretation: Input-agnostic saliency mapping of deep visual classifiers [28.28834523468462]
  Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. We show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. We introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs.
  arXiv  Detail & Related papers  (2023-03-31T06:58:45Z)
• Streamlining models with explanations in the learning loop [0.0]
  Several explainable AI methods allow a Machine Learning user to get insights on the classification process of a black-box model. We exploit this information to design a feature engineering phase, where we combine explanations with feature values.
  arXiv  Detail & Related papers  (2023-02-15T16:08:32Z)
• LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity [49.84167231111667]
  Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image. We introduce an approach to enhance the learning of dense equivariant representations in a self-supervised fashion. We show that having such a prior in the feature extractor helps in landmark detection, even under drastically limited number of annotations.
  arXiv  Detail & Related papers  (2022-04-06T17:48:18Z)
• Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation [22.688772441351308]
  Methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods provide lower resolution and blurry explanation maps that limit their explanation power. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique. We also propose a layer selection strategy that applies to the whole family of CNN-based models.
  arXiv  Detail & Related papers  (2020-10-01T20:27:30Z)
• Explanation-Guided Training for Cross-Domain Few-Shot Classification [96.12873073444091]
  Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. We introduce a novel training approach for existing FSC models. We show that explanation-guided training effectively improves the model generalization.
  arXiv  Detail & Related papers  (2020-07-17T07:28:08Z)
• Closed-Form Factorization of Latent Semantics in GANs [65.42778970898534]
  A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. We propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.
  arXiv  Detail & Related papers  (2020-07-13T18:05:36Z)

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.