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Chaofan Chen

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6 papers

1 author row

NeurIPS Conference 2025 Conference Paper

ProtoPairNet: Interpretable Regression through Prototypical Pair Reasoning

Rose Gurung
Ronilo Ragodos
Chiyu Ma
Tong Wang
Chaofan Chen

We present Prototypical Pair Network (ProtoPairNet), a novel interpretable architecture that combines deep learning with case-based reasoning to predict continuous targets. While prototype-based models have primarily addressed image classification with discrete outputs, extending these methods to continuous targets, such as regression, poses significant challenges. Existing architectures which rely heavily on one-to-one comparison with prototypes lack the directional information necessary for continuous predictions. Our method redefines the role of prototypes in such tasks by incorporating prototypical pairs into the reasoning process. Predictions are derived based on the input's relative dissimilarities to these pairs, leveraging an intuitive geometric interpretation. Our method further reduces the complexity of the reasoning process by relying on the single most relevant pair of prototypes, rather than all prototypes in the model as was done in prior works. Our model is versatile enough to be used in both vision-based regression and continuous control in reinforcement learning. Our experiments demonstrate that ProtoPairNet achieves performance on par with its black-box counterparts across these tasks. Comprehensive analyses confirm the meaningfulness of prototypical pairs and the faithfulness of our model’s interpretations, and extensive user studies highlight our model's improved interpretability over existing methods.

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AAAI Conference 2025 Conference Paper

Pseudo Informative Episode Construction for Few-Shot Class-Incremental Learning

Chaofan Chen
Xiaoshan Yang
Changsheng Xu

Few-Shot Class-Incremental Learning (FSCIL) studies how to empower the machine learning system to learn novel classes with only a few annotated examples continually. To tackle the FSCIL task, recent state-of-the-art methods propose to employ the meta-learning mechanism, which constructs the pseudo incremental episodes/tasks in the training phase. However, these methods only select part of the base classes to construct the pseudo novel classes in the feature space of the base classes, which cannot mimic the real novel classes of the testing scenario. To deal with this problem, we propose a new Pseudo Informative Episode Construction (PIEC) framework. Specifically, we first perform distribution-level mixing to generate a set of pseudo novel classes in the feature space of the novel class. Then, we propose two diversity criteria to select the informative pseudo novel classes that have large discrepancies with each other and high information gain over the base classes to construct the pseudo incremental session. In this way, we can allow the model to learn rich new concepts beyond the base classes as in the real incremental session during the episodic training procedure, thus improving its generalization ability. Extensive experiments on three popular classification benchmarks (i.e., CUB200, miniImageNet, and CIFAR100) show that the proposed framework can outperform other state-of-the-art methods.

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NeurIPS Conference 2024 Conference Paper

Interpretable Image Classification with Adaptive Prototype-based Vision Transformers

Chiyu Ma
Jon Donnelly
Wenjun Liu
Soroush Vosoughi
Cynthia Rudin
Chaofan Chen

We present ProtoViT, a method for interpretable image classification combining deep learning and case-based reasoning. This method classifies an image by comparing it to a set of learned prototypes, providing explanations of the form ``this looks like that. '' In our model, a prototype consists of parts, which can deform over irregular geometries to create a better comparison between images. Unlike existing models that rely on Convolutional Neural Network (CNN) backbones and spatially rigid prototypes, our model integrates Vision Transformer (ViT) backbones into prototype based models, while offering spatially deformed prototypes that not only accommodate geometric variations of objects but also provide coherent and clear prototypical feature representations with an adaptive number of prototypical parts. Our experiments show that our model can generally achieve higher performance than the existing prototype based models. Our comprehensive analyses ensure that the prototypes are consistent and the interpretations are faithful.

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NeurIPS Conference 2023 Conference Paper

This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations

Chiyu Ma
Brandon Zhao
Chaofan Chen
Cynthia Rudin

We present ProtoConcepts, a method for interpretable image classification combining deep learning and case-based reasoning using prototypical parts. Existing work in prototype-based image classification uses a "this looks like that'' reasoning process, which dissects a test image by finding prototypical parts and combining evidence from these prototypes to make a final classification. However, all of the existing prototypical part-based image classifiers provide only one-to-one comparisons, where a single training image patch serves as a prototype to compare with a part of our test image. With these single-image comparisons, it can often be difficult to identify the underlying concept being compared (e. g. , "is it comparing the color or the shape? ''). Our proposed method modifies the architecture of prototype-based networks to instead learn prototypical concepts which are visualized using multiple image patches. Having multiple visualizations of the same prototype allows us to more easily identify the concept captured by that prototype (e. g. , "the test image and the related training patches are all the same shade of blue''), and allows our model to create richer, more interpretable visual explanations. Our experiments show that our ``this looks like those'' reasoning process can be applied as a modification to a wide range of existing prototypical image classification networks while achieving comparable accuracy on benchmark datasets.

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NeurIPS Conference 2019 Conference Paper

This Looks Like That: Deep Learning for Interpretable Image Recognition

Chaofan Chen
Oscar Li
Daniel Tao
Alina Barnett
Cynthia Rudin
Jonathan Su

When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture -- prototypical part network (ProtoPNet), that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The model thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training without any annotations for parts of images. We demonstrate our method on the CUB-200-2011 dataset and the Stanford Cars dataset. Our experiments show that ProtoPNet can achieve comparable accuracy with its analogous non-interpretable counterpart, and when several ProtoPNets are combined into a larger network, it can achieve an accuracy that is on par with some of the best-performing deep models. Moreover, ProtoPNet provides a level of interpretability that is absent in other interpretable deep models.

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AAAI Conference 2018 Conference Paper

Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions

Oscar Li
Hao Liu
Chaofan Chen
Cynthia Rudin

Deep neural networks are widely used for classiﬁcation. These deep models often suffer from a lack of interpretability – they are particularly difﬁcult to understand because of their non-linear nature. As a result, neural networks are often treated as “black box” models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classiﬁcation process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.

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