Zero-shot Object-Level OOD Detection with Context-Aware Inpainting

Machine learning algorithms are increasingly provided as black-box cloud services or pre-trained models, without access to their training data. This motivates the problem of zero-shot out-of-distribution (OOD) detection. Concretely, we aim to detect OOD objects that do not belong to the classifier's label set but are erroneously classified as in-distribution (ID) objects. Our approach, RONIN, uses an off-the-shelf diffusion model to replace detected objects with inpainting. RONIN conditions the inpainting process with the predicted ID label, drawing the input object closer to the in-distribution domain. As a result, the reconstructed object is very close to the original in the ID cases and far in the OOD cases, allowing RONIN to effectively distinguish ID and OOD samples. Throughout extensive experiments, we demonstrate that RONIN achieves competitive results compared to previous approaches across several datasets, both in zero-shot and non-zero-shot settings.

Controllable Expensive Multi-objective Optimization with Warm-starting Gaussian Processes

Pareto Set Learning (PSL) is a promising approach for approximating the entire Pareto front in multi-objective optimization (MOO) problems. However, existing derivative-free PSL methods are often unstable and inefficient, especially for expensive black-box MOO problems where objective function evaluations are costly. In this work, we propose to address the instability and inefficiency of existing PSL methods with a novel controllable PSL method, called Co-PSL. Particularly, Co-PSL consists of two stages: (1) warm-starting Bayesian optimization to obtain quality Gaussian Processes priors and (2) controllable Pareto set learning to accurately acquire a parametric mapping from preferences to the corresponding Pareto solutions. The former is to help stabilize the PSL process and reduce the number of expensive function evaluations. The latter is to support real-time trade-off control between conflicting objectives. Performances across synthesis and real-world MOO problems showcase the effectiveness of our Co-PSL for expensive multi-objective optimization tasks.

Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment

Question answering (QA) in law is a challenging problem because legal documents are much more complicated than normal texts in terms of terminology, structure, and temporal and logical relationships. It is even more difficult to perform legal QA for low-resource languages like Vietnamese where labeled data are rare and pre-trained language models are still limited. In this paper, we try to overcome these limitations by implementing a Vietnamese article-level retrieval-based legal QA system and introduce a novel method to improve the performance of language models by improving data quality through weak labeling. Our hypothesis is that in contexts where labeled data are limited, efficient data enrichment can help increase overall performance. Our experiments are designed to test multiple aspects, which demonstrate the effectiveness of the proposed technique.

Law to Binary Tree -- An Formal Interpretation of Legal Natural Language

Knowledge representation and reasoning in law are essential to facilitate the automation of legal analysis and decision-making tasks. In this paper, we propose a new approach based on legal science, specifically legal taxonomy, for representing and reasoning with legal documents. Our approach interprets the regulations in legal documents as binary trees, which facilitates legal reasoning systems to make decisions and resolve logical contradictions. The advantages of this approach are twofold. First, legal reasoning can be performed on the basis of the binary tree representation of the regulations. Second, the binary tree representation of the regulations is more understandable than the existing sentence-based representations. We provide an example of how our approach can be used to interpret the regulations in a legal document.

Enhancing Few-shot Image Classification with Cosine Transformer

This paper addresses the few-shot image classification problem. One notable limitation of few-shot learning is the variation in describing the same category, which might result in a significant difference between small labeled support and large unlabeled query sets. Our approach is to obtain a relation heatmap between the two sets in order to label the latter one in a transductive setting manner. This can be solved by using cross-attention with the scaled dot-product mechanism. However, the magnitude differences between two separate sets of embedding vectors may cause a significant impact on the output attention map and affect model performance. We tackle this problem by improving the attention mechanism with cosine similarity. Specifically, we develop FS-CT (Few-shot Cosine Transformer), a few-shot image classification method based on prototypical embedding and transformer-based framework. The proposed Cosine attention improves FS-CT performances significantly from nearly 5% to over 20% in accuracy compared to the baseline scaled dot-product attention in various scenarios on three few-shot datasets mini-ImageNet, CUB-200, and CIFAR-FS. Additionally, we enhance the prototypical embedding for categorical representation with learnable weights before feeding them to the attention module. Our proposed method FS-CT along with the Cosine attention is simple to implement and can be applied for a wide range of applications. Our codes are available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer