Elnatan Kadar, Guy Gilboa

arxiv preprint

We propose a new way to explain and to visualize neural network classification through a decomposition-based explainable AI (DXAI). Instead of providing an explanation heatmap, our method yields a decomposition of the image into class-agnostic and class-distinct parts, with respect to the data and chosen classifier. Following a fundamental signal processing paradigm of analysis and synthesis, the original image is the sum of the decomposed parts. We thus obtain a radically different way of explaining classification. The class-agnostic part ideally is composed of all image features which do not posses class information, where the class-distinct part is its complementary. This new visualization can be more helpful and informative in certain scenarios, especially when the attributes are dense, global and additive in nature, for instance, when colors or textures are essential for class distinction.

Yossef Meir Levi, Guy Gilboa

The ability to cope accurately and fast with Out-Of-Distribution (OOD) samples is crucial in real-world safety demanding applications. In this work we first study the interplay between critical points of 3D point clouds and OOD samples. Our findings are that common corruptions and outliers are often interpreted as critical points. We generalize the notion of critical points into importance measures. We show that training a classification network based only on less important points dramatically improves robustness, at a cost of minor performance loss on the clean set. We observe that normalized entropy is highly informative for corruption analysis. An adaptive threshold based on normalized entropy is suggested for selecting the set of uncritical points. Our proposed importance measure is extremely fast to compute. We show it can be used for a variety of applications, such as Explainable AI (XAI), Outlier Removal, Uncertainty Estimation, Robust Classification and Adversarial Defense. We reach SOTA results on the two latter tasks.

arxiv preprint

 

Or Streicher, Ido Cohen, Guy Gilboa; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 10396-10405

CVPR Repository

Arxiv preprint 

Graph is a highly generic and diverse representation, suitable for almost any data processing problem. Spectral graph theory has been shown to provide powerful algorithms, backed by solid linear algebra theory. It thus can be extremely instrumental to design deep network building blocks with spectral graph characteristics. For instance, such a network allows the design of optimal graphs for certain tasks or obtaining a canonical orthogonal low-dimensional embedding of the data. Recent attempts to solve this problem were based on minimizing Rayleigh-quotient type losses. We propose a different approach of directly learning the eigensapce. A severe problem of the direct approach, applied in batch-learning, is the inconsistent mapping of features to eigenspace coordinates in different batches. We analyze the degrees of freedom of learning this task using batches and propose a stable alignment mechanism that can work both with batch changes and with graph-metric changes. We show that our learnt spectral embedding is better in terms of NMI, ACC, Grassman distance, orthogonality and classification accuracy, compared to SOTA. In addition, the learning is more stable.

Or Streicher, Guy Gilboa, accepted to SSVM 2023 (oral)

9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21–25, 2023, Proceedings, Springer LNCS 14009, pp. 250-262, 2023.

Springer conference proceedings

Arxiv preprint

Abatract

Semi-supervised learning is highly useful in common scenarios
where labeled data is scarce but unlabeled data is abundant. The
graph (or nonlocal) Laplacian is a fundamental smoothing operator for
solving various learning tasks. For unsupervised clustering, a spectral embedding
is often used, based on graph-Laplacian eigenvectors. For semisupervised
problems, the common approach is to solve a constrained
optimization problem, regularized by a Dirichlet energy, based on the
graph-Laplacian. However, as supervision decreases, Dirichlet optimization
becomes suboptimal. We therefore would like to obtain a smooth
transition between unsupervised clustering and low-supervised graphbased
classification.
In this paper, we propose a new type of graph-Laplacian which is adapted
for Semi-Supervised Learning (SSL) problems. It is based on both density
and contrastive measures and allows the encoding of the labeled data directly
in the operator. Thus, we can perform successfully semi-supervised
learning using spectral clustering. The benefits of our approach are illustrated
for several SSL problems.