Recent advancements in autonomous driving have seen a paradigm shift towards end-to-end learning paradigms, which map sensory inputs directly to driving actions, thereby enhancing the robustness and adaptability of autonomous vehicles. However, these models often sacrifice interpretability, posing significant challenges to trust, safety, and regulatory compliance. To address these issues, we introduce DRIVE – Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving, a comprehensive framework designed to improve the dependability and stability of explanations in end-to-end unsupervised autonomous driving models. Our work specifically targets the inherent instability problems observed in the Driving through the Concept Gridlock (DCG) model, which undermine the trustworthiness of its explanations and decisionmaking processes. We define four key attributes of DRIVE: consistent interpretability, stable interpretability, consistent output, and stable output. These attributes collectively ensure that explanations remain reliable and robust across different scenarios and perturbations. Through extensive empirical evaluations, we demonstrate the effectiveness of our framework in enhancing the stability and dependability of explanations, thereby addressing the limitations of current models. Our contributions include an in-depth analysis of the dependability issues within the DCG model, a rigorous definition of DRIVE with its fundamental properties, a framework to implement DRIVE, and novel metrics for evaluating the dependability of concept-based explainable autonomous driving models. These advancements lay the groundwork for the development of more reliable and trusted autonomous driving systems, paving the way for their broader acceptance and deployment in real-world applications. “We can only see a short distance ahead, but we can see plenty there that needs to be done.” – Alan Turing

How far are we really from automatically generating neural networks? While neural network weight generation shows promise, current approaches struggle with generalization to unseen tasks and practical application exploration. To address this, we propose T2W, a diffusion transformer framework that generates task-specific weights conditioned on natural language descriptions. T2W hierarchically processes network parameters into uniform blocks, integrates text embeddings from CLIP via a prior attention mechanism, and employs adversarial training with weight-space augmentation to enhance generalization. Experiments on Cifar100, Caltech256, and TinyImageNet demonstrate T2W's ability to produce high-quality weights for unseen tasks, outperforming optimization-based initialization and enabling novel applications such as weight enhancement and text-guided model fusion. Our work bridges textual semantics with weight-space dynamics, supported by an open-source dataset of text-weight pairs, advancing the practicality of generative models in neural network parameter synthesis. Our code is available on https://github.com/TianSuya/T2W.

Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics. However, the existing parameter inference method like Monte Carlo sampling is quite time-consuming. In this paper, we propose two efficient neural mixed counting models, i.e., the Negative Binomial-Neural Topic Model (NB-NTM) and the Gamma Negative Binomial-Neural Topic Model (GNB-NTM) for dispersed topic discovery. Neural variational inference algorithms are developed to infer model parameters by using the reparameterization of Gamma distribution and the Gaussian approximation of Poisson distribution. Experiments on real-world datasets indicate that our models outperform state-of-the-art baseline models in terms of perplexity and topic coherence. The results also validate that both NB-NTM and GNB-NTM can produce explainable intermediate variables by generating dispersed proportions of document topics.

Video satellite is of great significance in change detection and military reconnaissance due to its high temporal resolution. However, restricted by the hardware conditions, spatial resolution must be sacrificed if temporal resolution is to be guaranteed. Because of this, how to reconstruct super resolution (SR) video satellite data is particularly important. Based on the proposed SR Residual Network (SRResNet), we proposed a Pyramid Residual Network (PySRResNet) model, using a pyramid structure to obtain features of different scales, including 1, 1/2 and 1/4, and concatenate them together to provide more detailed information for SR reconstruction. In addition, we reduced the number of blocks and removed the batch normalization layer to achieve good performance. Training with “Jilin-1” video satellite images, our PySRResNet can get superior grades than other comparing models both in PSNR and SSIM, which demonstrates the effectiveness of PySRResNet in video satellite imagery SR reconstruction.

Recently, as a new remote sensing system, video satellite develops rapidly for long-time observation. Thanks to its high temporal resolution, video satellite has been extensively used for environmental detection, especially for dynamic target monitoring. However, limited by the imaging device, it sacrifices some of its spatial resolution. Therefore, the super-resolution (SR) technology applied to these images is crucial. Based on deep residual learning, which has obtained a great success in the single-image SR, we propose a SR network structure which consists of two main steps. First, we use multi-scale feature extraction to exploit more contextual information on video satellite imagery, which is aimed at inferring high frequency components. Then, we utilize a series of residual blocks to learn the mapping between low resolution and high resolution images in a deeper and more stable network. In our experiment, the SR reconstruction results on Jinlin-1 satellite images greatly indicate the effectiveness of our method and the potential of the residual network for video satellite imagery SR.