Face cartoonization remains a challenging task due to significant geometric deformations between facial photos and cartoons, as well as the absence of paired training data for supervised learning. Existing methods struggle to generate high-quality cartoonized avatars with attribute consistency. To address this challenge, this paper proposes an unsupervised facial cartoonization method based on cross-domain aligned and deformable vector quantization (CADQ). Firstly, we construct textual descriptions with facial attributes for both photo datasets and cartoon collections. Attribute consistency during transformation is enforced through individually contrastive learning between image-text cross-modal features and globally distribution alignment across photo-cartoon domains. Secondly, a deformable Transformer with dual attention is introduced during the transformation process, which queries corresponding cartoon code-book entries based on image features to simulate cross-domain geometric deformations. Experimental results demonstrate that the proposed method can convert facial photos into high-quality cartoons with attribute consistency, outperforming existing state-of-the-art approaches. Furthermore, the method can be effectively extended to unsupervised cross-domain generation of other artistic portrait styles, achieving superior or highly competitive performance. Our code has been released at: https://github.com/IIP-Lab- XDU/CADQ.

Approximate Nearest Neighbor (ANN) search is widely used in applications such as recommendation systems, search engines, and natural language processing. Indexing techniques like the Inverted File (IVF) offer efficiency at the cost of accuracy, yet lack formal mechanisms to quantify or control approximation error. Existing approaches that attempt to provide such guarantees typically rely on restrictive assumptions about underlying data distributions, which limits their generalizability. We introduce ConANN, the first framework to provide formal, distribution-free error guarantees for IVF-based ANN search by leveraging recent advances in Conformal Risk Control. Empirical evaluation across five standard benchmarks demonstrates that ConANN: (1) tightly controls approximation error, achieving a worst-case False Negative Rate deviation within 0.03 percentage points of the target; (2) provides formal guarantees without requiring expansion of the search space, and in some cases even reduces the number of probed clusters; (3) dynamically adapts the cluster probes required per query; and (4) incurs negligible overheads when compared to existing state-of-the-art baselines. ConANN is integrated into the FAISS vector search library, facilitating adoption in real-world ANN systems.

Most techniques for explainable machine learning focus on a single modality for the explanations, e.g., using either feature or example attribution. A novel approach, called Hybrid Attribution Network (HAN), is proposed for providing multimodal explanations for image classification. The technique first extracts embeddings from a deep neural network (DNN), which are subsequently used by a KNN classifier to form predictions; example attributions can then be derived from the latter. Based on the example attributions, pixel attributions are further generated to provide complementary feature-level explanations. Results from an empirical investigation show that HAN may provide highly concentrated example attributions, i.e., the predictions can be explained with few training examples, without compromising predictive performance relative to the original deep neural network. Moreover, the pixel attributions are shown to enhance the interpretability of the predictions, by highlighting key pixels in the example attributions. An important finding from the empirical investigation is that the choice of layer to use for the embeddings may have a large impact on both the predictive performance and the generated explanations.

Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their blackbox behavior poses a problem and restricts their usability. To overcome this restriction, we propose XDCCA (eXplanations for Deep Canonical Correlation Analysis), an explanation strategy using characteristic rules in combination with SHAP that exploits the inherent structure of latent spaces created by correlation maximization techniques. We demonstrate how XDCCA allows for interpreting learned representations and their correlation using real medical time series and synthetic image data.

Most techniques for explainable machine learning focus on feature attribution, i.e., values are assigned to the features such that their sum equals the prediction. Example attribution is another form of explanation that assigns weights to the training examples, such that their scalar product with the labels equals the prediction. The latter may provide valuable complementary information to feature attribution, in particular in cases where the features are not easily interpretable. Current example-based explanation techniques have targeted a few model types only, such as k-nearest neighbors and random forests. In this work, a technique for obtaining example-based explanations from deep neural networks (EBE-DNN) is proposed. The basic idea is to use the deep neural network to obtain an embedding, which is employed by a k-nearest neighbor classifier to form a prediction; the example attribution can hence straightforwardly be derived from the latter. Results from an empirical investigation show that EBE-DNN can provide highly concentrated example attributions, i.e., the predictions can be explained with few training examples, without reducing accuracy compared to the original deep neural network. Another important finding from the empirical investigation is that the choice of layer to use for the embeddings may have a large impact on the resulting accuracy.

Shapley values have several desirable, theoretically well-supported, properties for explaining black-box model predictions. Traditionally, Shapley values are computed post-hoc, leading to additional computational cost at inference time. To overcome this, a novel method, called ViaSHAP, is proposed, that learns a function to compute Shapley values, from which the predictions can be derived directly by summation. Two approaches to implement the proposed method are explored; one based on the universal approximation theorem and the other on the Kolmogorov-Arnold representation theorem. Results from a large-scale empirical investigation are presented, showing that ViaSHAP using Kolmogorov-Arnold Networks performs on par with state-of-the-art algorithms for tabular data. It is also shown that the explanations of ViaSHAP are significantly more accurate than the popular approximator FastSHAP on both tabular data and images.