This paper proposes a fast and general-purpose image restoration method. The key idea is to achieve few-step or even one-step inference by conducting consistency distilling or training on a specific mean-reverting stochastic differential equations. Furthermore, based on this, we propose a novel linear-nonlinear decoupling training strategy, significantly enhancing training effectiveness and surpassing consistency distillation on inference performance. This allows our method to be independent of any pre-trained checkpoint, enabling it to serve as an effective standalone imageto-image transformation model. Finally, to avoid trivial solutions and stabilize model training, we introduce a simple origin-guided loss. To validate the effectiveness of our proposed method, we conducted experiments on tasks including image deraining, denoising, deblurring, and low-light image enhancement. The experiments show that our method achieves highly competitive results with only one-step inference. And with just two-step inference, it can achieve state-of-the-art performance in low-light image enhancement. Furthermore, a number of ablation experiments demonstrate the effectiveness of the proposed training strategy.

We appreciate positive and constructive comments, and address the main concerns raised by the reviewers below. Table 1: Accuracies [%] of baseline and proposed models with different meta-class set configurations on CUB-200. Final vs. best accuracy [R1] The results from all algorithms are given by the models identified using a clean We will present more detailed results if our paper is accepted. Others [All] We will supplement the missing details and results in the final manuscript if our paper is accepted.

However, this convention tends to oversimplify the multidimensional and heterogeneous nature of human preferences, leading to reduced expressivity and even misalignment. This paper presents Panacea, an innovative approach that reframes alignment as a multi-dimensional preference optimization problem. Panacea trains a single model capable of adapting online and Paretooptimally to diverse sets of preferences without the need for further tuning. A major challenge here is using a low-dimensional preference vector to guide the model's behavior, despite it being governed by an overwhelmingly large number of parameters. To address this, Panacea is designed to use singular value decomposition (SVD)-based low-rank adaptation, which allows the preference vector to be simply injected online as singular values. Theoretically, we prove that Panacea recovers the entire Pareto front with common loss aggregation methods under mild conditions. Moreover, our experiments demonstrate, for the first time, the feasibility of aligning a single LLM to represent an exponentially vast spectrum of human preferences through various optimization methods. Our work marks a step forward in effectively and efficiently aligning models to diverse and intricate human preferences in a controllable and Pareto-optimal manner.

We compared the influence of these approximations. Figure 2(b) illustrates that these approximations are not affecting the convergence rate in the sample efficiency. However, when compared to the wall-clock time (Figure 2(c)), the exact sampler without the factorisation trick is apparently slow to converge. Moreover, the provable recombination algorithm is slower than an LP solver implementation. Thus, the number of samples the provable recombination algorithm per wall time is much smaller than the LP solver. Therefore, our BASQ standard solver delivers solid empirical performance. Qualitative evaluation of posterior inference Figure 3 shows the qualitative evaluation of joint posterior inference after 200 seconds passed against the analytical true posterior. The estimated posterior shape is exactly the same as the ground truth.

We investigate learning the eigenfunctions of evolution operators for time-reversal invariant stochastic processes, a prime example being the Langevin equation used in molecular dynamics. Many physical or chemical processes described by this equation involve transitions between metastable states separated by high potential barriers that can hardly be crossed during a simulation. To overcome this bottleneck, data are collected via biased simulations that explore the state space more rapidly. We propose a framework for learning from biased simulations rooted in the infinitesimal generator of the process and the associated resolvent operator. We contrast our approach to more common ones based on the transfer operator, showing that it can provably learn the spectral properties of the unbiased system from biased data. In experiments, we highlight the advantages of our method over transfer operator approaches and recent developments based on generator learning, demonstrating its effectiveness in estimating eigenfunctions and eigenvalues. Importantly, we show that even with datasets containing only a few relevant transitions due to sub-optimal biasing, our approach recovers relevant information about the transition mechanism.

Facial expression and action units (AUs) represent two levels of descriptions of the facial behavior. Due to the underlying facial anatomy and the need to form a meaningful coherent expression, they are strongly correlated. This paper proposes to systematically capture their dependencies and incorporate them into a deep learning framework for joint facial expression recognition and action unit detection. Specifically, we first propose a constraint optimization method to encode the generic knowledge on expression-AUs probabilistic dependencies into a Bayesian Network (BN). The BN is then integrated into a deep learning framework as a weak supervision for an AU detection model.

We deal with the selective classification problem (supervised-learning problem with a rejection option), where we want to achieve the best performance at a certain level of coverage of the data. We transform the original m-class classification problem to (m + 1)-class where the (m + 1)-th class represents the model abstaining from making a prediction due to disconfidence. Inspired by portfolio theory, we propose a loss function for the selective classification problem based on the doubling rate of gambling. Minimizing this loss function corresponds naturally to maximizing the return of a horse race, where a player aims to balance between betting on an outcome (making a prediction) when confident and reserving one's winnings (abstaining) when not confident. This loss function allows us to train neural networks and characterize the disconfidence of prediction in an end-to-end fashion. In comparison with previous methods, our method requires almost no modification to the model inference algorithm or model architecture. Experiments show that our method can identify uncertainty in data points, and achieves strong results on SVHN and CIFAR10 at various coverages of the data.

We provide comprehensive supplementary materials for better understanding of our paper and show more evidence to support our idea. The appendices are organized as follows: Sec. A first provides some discussions for certain points. Then we further provide detailed experiment settings, results, analysis and visualizations in Sec. B. Finally, we show details for STL-C and ConceptFactory asset in Sec. C. A.1 Purpose behind ConceptFactory In this paper, we present the idea of ConceptFactory to facilitate more efficient annotation of 3D object knowledge by recognizing 3D objects through generalized concepts. We would like to emphasize that our purpose mainly focuses on providing an advanced practice in annotation collection.

We present ConceptFactory, a novel scope to facilitate more efficient annotation of 3D object knowledge by recognizing 3D objects through generalized concepts (i.e.

We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices.