Diffusion models have emerged as highly effective techniques for inpainting, however, they remain constrained by slow sampling rates. While recent advances have enhanced generation quality, they have also increased sampling time, thereby limiting scalability in real-world applications. We investigate the generative sampling process of diffusion-based inpainting models and observe that these models make minimal use of the input condition during the initial sampling steps. As a result, the sampling trajectory deviates from the data manifold, requiring complex synchronization mechanisms to realign the generation process. To address this, we propose Time-aware Diffusion Paint (TD-Paint), a novel approach that adapts the diffusion process by modeling variable noise levels at the pixel level. This technique allows the model to efficiently use known pixel values from the start, guiding the generation process toward the target manifold. By embedding this information early in the diffusion process, TD-Paint significantly accelerates sampling without compromising image quality. Unlike conventional diffusion-based inpainting models, which require a dedicated architecture or an expensive generation loop, TD-Paint achieves faster sampling times without architectural modifications. Experimental results across three datasets show that TD-Paint outperforms state-of-the-art diffusion models while maintaining lower complexity.

In this paper, we study the problem of multi-domain translation: given an element (a) of domain A, we wish to generate a corresponding element (b) in another domain B, and vice versa. Acquiring supervision in multiple domains can be a tedious task, also we propose to learn this translation from one domain to another when supervision is available as a pair (a, b), and leverage possible unpaired data when only (a) or only (b) is available. We introduce a new unified framework called Latent Space Mapping (LSM), which exploits the manifold assumption to learn a latent space from each domain. Unlike existing approaches, we propose to further regularize each latent space using available domains by learning each dependency between pairs of domains. We evaluate our approach on three tasks performing i) a synthetic dataset with image translation, ii) a real-world task of semantic segmentation for medical images, and iii) a real-world task of facial landmark detection.

Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.

In many real-world classification tasks, it is crucial to take into account misclassification costs for designing an accurate classification system. Nevertheless, begin able to reject a sample is also often needed in order to avoid a very risky prediction error. In that case, a cost-sensitive classifier must embed a rejection mechanism, that takes into account the rejection costs as well as the misclassification costs. In binary classification, the ROC space has shown to be very powerful for designing cost-sensitive classifiers, but it has been poorly exploited for designing classifiers able to reject. The purpose of this work is to extend a ROC-based ensemble method recently proposed, called the ROC Front method, with a cost-sensitive rejection mechanism. This approach compares favorably to the state-of-the-art ROC-based rejection rule recently proposed for binary cost-sensitive classification. It is also more robust as it allows to design an accurate classifier for all cost-sensitive situations contrary to the state-of-the-art method that fails in many cases, as for example with small datasets.

In this paper we present a study on the random forest (RF) family of ensemble methods. In a ldquoclassicalrdquo RF induction process a fixed number of randomized decision trees are inducted to form an ensemble. This kind of algorithm presents two main drawbacks : (i) the number of trees has to be fixed a priori (ii) the interpretability and analysis capacities offered by decision tree classifiers are lost due to the randomization principle. This kind of process in which trees are independently added to the ensemble, offers no guarantee that all those trees will cooperate effectively in the same committee. This statement rises two questions: are there any decision trees in a RF that provide the deterioration of ensemble performance? If so, is it possible to form a more accurate committee via removal of decision trees with poor performance? The answer to these questions is tackled as a classifier selection problem. We thus show that better subsets of decision trees can be obtained even using a sub-optimal classifier selection method. This proves that ldquoclassicalrdquo RF induction process, for which randomized trees are arbitrary added to the ensemble, is not the best approach to produce accurate RF classifiers. We also show the interest in designing RF by adding trees in a more dependent way than it is traditionally done in ldquoclassicalrdquo RF induction algorithms.

In the pattern recognition field, growing interest has been shown in recent years for multiple classifier systems and particularly for bagging, boosting and random sub-spaces. Those methods aim at inducing an ensemble of classifiers by producing diversity at different levels. Following this principle, Breiman has introduced in 2001 another family of methods called random forest. Our work aims at studying those methods in a strictly pragmatic approach, in order to provide rules on parameter settings for practitioners. For that purpose we have experimented the forest-RI algorithm, considered as the random forest reference method, on the MNIST handwritten digits database. In this paper, we describe random forest principles and review some methods proposed in the literature. We present next our experimental protocol and results. We finally draw some conclusions on random forest global behavior according to their parameter tuning.