Multi-terrain cost-efficient path planning is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes travel costs. This is especially crucial for real-world applications where robots need to navigate diverse terrains in outdoor environments, where recharging or refueling is difficult. However, there is very limited research on this topic. In this paper, we develop a prompt-based approach, LLM-Advisor, which leverages large language models (LLMs) as effective advisors for path planning. The LLM-Advisor selectively provides suggestions, demonstrating its ability to recognize when no modifications are necessary. When suggestions are made, 70.59% of the paths suggested for the A* algorithm, 69.47% for the RRT* algorithm, and 78.70% for the LLM-A* algorithm achieve greater cost efficiency. Since LLM-Advisor may occasionally lack common sense in their suggestions, we propose two hallucination-mitigation strategies. Furthermore, we experimentally verified that GPT-4o performs poorly in zero-shot path planning, even when terrain descriptions are clearly provided, demonstrating its low spatial awareness. We also experimentally demonstrate that using an LLM as an advisor is more effective than directly integrating it into the path-planning loop. Since LLMs may generate hallucinations, using LLMs in the loop of a search-based method (such as A*) may lead to a higher number of failed paths, demonstrating that our proposed LLM-Advisor is a better choice.

Continual Learning (CL) addresses the problem of learning from a data sequence where the distribution changes over time. Recently, efficient solutions leveraging Pre-Trained Models (PTM) have been widely explored in the offline CL (offCL) scenario, where the data corresponding to each incremental task is known beforehand and can be seen multiple times. However, such solutions often rely on 1) prior knowledge regarding task changes and 2) hyper-parameter search, particularly regarding the learning rate. Both assumptions remain unavailable in online CL (onCL) scenarios, where incoming data distribution is unknown and the model can observe each datum only once. Therefore, existing offCL strategies fall largely behind performance-wise in onCL, with some proving difficult or impossible to adapt to the online scenario. In this paper, we tackle both problems by leveraging Online Prototypes (OP) and Class-Wise Hypergradients (CWH). OP leverages stable output representations of PTM by updating its value on the fly to act as replay samples without requiring task boundaries or storing past data. CWH learns class-dependent gradient coefficients during training to improve over sub-optimal learning rates. We show through experiments that both introduced strategies allow for a consistent gain in accuracy when integrated with existing approaches. We will make the code fully available upon acceptance.

The recent success of denoising diffusion models has significantly advanced text-to-image generation. While these large-scale pretrained models show excellent performance in general image synthesis, downstream objectives often require fine-tuning to meet specific criteria such as aesthetics or human preference. Reward gradient-based strategies are promising in this context, yet existing methods are limited to single-reward tasks, restricting their applicability in real-world scenarios that demand adapting to multiple objectives introduced incrementally over time. In this paper, we first define this more realistic and unexplored problem, termed Reward Incremental Learning (RIL), where models are desired to adapt to multiple downstream objectives incrementally. Additionally, while the models adapt to the ever-emerging new objectives, we observe a unique form of catastrophic forgetting in diffusion model fine-tuning, affecting both metric-wise and visual structure-wise image quality. To address this catastrophic forgetting challenge, we propose Reward Incremental Distillation (RID), a method that mitigates forgetting with minimal computational overhead, enabling stable performance across sequential reward tasks. The experimental results demonstrate the efficacy of RID in achieving consistent, high-quality generation in RIL scenarios. The source code of our work will be publicly available upon acceptance.

Image generation has shown remarkable results in generating high-fidelity realistic images, in particular with the advancement of diffusion-based models. However, the prevalence of AI-generated images may have side effects for the machine learning community that are not clearly identified. Meanwhile, the success of deep learning in computer vision is driven by the massive dataset collected on the Internet. The extensive quantity of synthetic data being added to the Internet would become an obstacle for future researchers to collect "clean" datasets without AI-generated content. Prior research has shown that using datasets contaminated by synthetic images may result in performance degradation when used for training. In this paper, we investigate the potential impact of contaminated datasets on Online Continual Learning (CL) research. We experimentally show that contaminated datasets might hinder the training of existing online CL methods. Also, we propose Entropy Selection with Real-synthetic similarity Maximization (ESRM), a method to alleviate the performance deterioration caused by synthetic images when training online CL models. Experiments show that our method can significantly alleviate performance deterioration, especially when the contamination is severe.

Adversarial examples have raised several open questions, such as why they can deceive classifiers and transfer between different models. A prevailing hypothesis to explain these phenomena suggests that adversarial perturbations appear as random noise but contain class-specific features. This hypothesis is supported by the success of perturbation learning, where classifiers trained solely on adversarial examples and the corresponding incorrect labels generalize well to correctly labeled test data. Although this hypothesis and perturbation learning are effective in explaining intriguing properties of adversarial examples, their solid theoretical foundation is limited. In this study, we theoretically explain the counterintuitive success of perturbation learning. We assume wide two-layer networks and the results hold for any data distribution. We prove that adversarial perturbations contain sufficient class-specific features for networks to generalize from them. Moreover, the predictions of classifiers trained on mislabeled adversarial examples coincide with those of classifiers trained on correctly labeled clean samples.

It is not fully understood why adversarial examples can deceive neural networks and transfer between different networks. To elucidate this, several studies have hypothesized that adversarial perturbations, while appearing as noises, contain class features. This is supported by empirical evidence showing that networks trained on mislabeled adversarial examples can still generalize well to correctly labeled test samples. However, a theoretical understanding of how perturbations include class features and contribute to generalization is limited. In this study, we provide a theoretical framework for understanding learning from perturbations using a one-hidden-layer network trained on mutually orthogonal samples. Our results highlight that various adversarial perturbations, even perturbations of a few pixels, contain sufficient class features for generalization. Moreover, we reveal that the decision boundary when learning from perturbations matches that from standard samples except for specific regions under mild conditions. The code is available at https://github.com/s-kumano/ It is well known that a small malicious perturbation, or an adversarial perturbation, can change a classifier's prediction from the correct class to an incorrect class (Szegedy et al., 2014). An interesting observation by Ilyas et al. (2019) has shown that a neural network, trained on adversarial examples labeled by such incorrect classes, can generalize to correctly labeled test samples. Definition 1.1 (Learning from adversarial perturbations (later redefined) (Ilyas et al., 2019)). Let f be a classifier trained on D. For each n, an adversarial example x Counterintuitively, networks can learn to classify correctly labeled test samples (e.g., a cat image with a cat label) from such mislabeled adversarial samples. The unexpected success of training on mislabeled adversarial examples suggests that adversarial perturbations may contain label-aligned features. For example, frog adversarial images labeled as horses have perturbations that appear as noises to humans but contain horse features.

Earthquakes are among the most immediate and deadly natural disasters that humans face. Accurately forecasting the extent of earthquake damage and assessing potential risks can be instrumental in saving numerous lives. In this study, we developed linear regression models capable of predicting seismic intensity distributions based on earthquake parameters: location, depth, and magnitude. Because it is completely data-driven, it can predict intensity distributions without geographical information. The dataset comprises seismic intensity data from earthquakes that occurred in the vicinity of Japan between 1997 and 2020, specifically containing 1,857 instances of earthquakes with a magnitude of 5.0 or greater, sourced from the Japan Meteorological Agency. We trained both regression and classification models and combined them to take advantage of both to create a hybrid model. The proposed model outperformed commonly used Ground Motion Prediction Equations (GMPEs) in terms of the correlation coefficient, F1 score, and MCC. Furthermore, the proposed model can predict even abnormal seismic intensity distributions, a task at conventional GMPEs often struggle.

Online Continual Learning (CL) solves the problem of learning the ever-emerging new classification tasks from a continuous data stream. Unlike its offline counterpart, in online CL, the training data can only be seen once. Most existing online CL research regards catastrophic forgetting (i.e., model stability) as almost the only challenge. In this paper, we argue that the model's capability to acquire new knowledge (i.e., model plasticity) is another challenge in online CL. While replay-based strategies have been shown to be effective in alleviating catastrophic forgetting, there is a notable gap in research attention toward improving model plasticity. To this end, we propose Collaborative Continual Learning (CCL), a collaborative learning based strategy to improve the model's capability in acquiring new concepts. Additionally, we introduce Distillation Chain (DC), a novel collaborative learning scheme to boost the training of the models. We adapted CCL-DC to existing representative online CL works. Extensive experiments demonstrate that even if the learners are well-trained with state-of-the-art online CL methods, our strategy can still improve model plasticity dramatically, and thereby improve the overall performance by a large margin.

Online Continual Learning (OCL) addresses the problem of training neural networks on a continuous data stream where multiple classification tasks emerge in sequence. In contrast to offline Continual Learning, data can be seen only once in OCL. In this context, replay-based strategies have achieved impressive results and most state-of-the-art approaches are heavily depending on them. While Knowledge Distillation (KD) has been extensively used in offline Continual Learning, it remains under-exploited in OCL, despite its potential. In this paper, we theoretically analyze the challenges in applying KD to OCL. We introduce a direct yet effective methodology for applying Momentum Knowledge Distillation (MKD) to many flagship OCL methods and demonstrate its capabilities to enhance existing approaches. In addition to improving existing state-of-the-arts accuracy by more than $10\%$ points on ImageNet100, we shed light on MKD internal mechanics and impacts during training in OCL. We argue that similar to replay, MKD should be considered a central component of OCL.

Deep neural networks have shown remarkable performance when trained on independent and identically distributed data from a fixed set of classes. However, in real-world scenarios, it can be desirable to train models on a continuous stream of data where multiple classification tasks are presented sequentially. This scenario, known as Continual Learning (CL) poses challenges to standard learning algorithms which struggle to maintain knowledge of old tasks while learning new ones. This stability-plasticity dilemma remains central to CL and multiple metrics have been proposed to adequately measure stability and plasticity separately. However, none considers the increasing difficulty of the classification task, which inherently results in performance loss for any model. In that sense, we analyze some limitations of current metrics and identify the presence of setup-induced forgetting. Therefore, we propose new metrics that account for the task's increasing difficulty. Through experiments on benchmark datasets, we demonstrate that our proposed metrics can provide new insights into the stability-plasticity trade-off achieved by models in the continual learning environment.