Typical R-convolution graph kernels invoke the kernel functions that decompose graphs into non-isomorphic substructures and compare them. However, overlooking implicit similarities and topological position information between those substructures limits their performances. In this paper, we introduce Deep Hierarchical Graph Alignment Kernels (DHGAK) to resolve this problem. Specifically, the relational substructures are hierarchically aligned to cluster distributions in their deep embedding space. The substructures belonging to the same cluster are assigned the same feature map in the Reproducing Kernel Hilbert Space (RKHS), where graph feature maps are derived by kernel mean embedding. Theoretical analysis guarantees that DHGAK is positive semi-definite and has linear separability in the RKHS. Comparison with state-of-the-art graph kernels on various benchmark datasets demonstrates the effectiveness and efficiency of DHGAK. The code is available at Github (https://github.com/EWesternRa/DHGAK).
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Offline reinforcement learning (RL) algorithms are applied to learn performant, well-generalizing policies when provided with a static dataset of interactions. Many recent approaches to offline RL have seen substantial success, but with one key caveat: they demand substantial per-dataset hyperparameter tuning to achieve reported performance which requires policy rollouts in the environment to evaluate; this can rapidly become cumbersome. Furthermore, substantial tuning requirements can hamper the adoption of these algorithms in practical domains. In this paper, we present TD3 with Behavioral Supervisor Tuning (TD3-BST), an algorithm that trains an uncertainty model and uses it to guide the policy to select actions within the dataset support. TD3-BST can learn more effective policies from offline datasets compared to prior methods and achieves the best performance across challenging benchmarks without requiring per-dataset tuning.
The paper studies sequential reasoning over graph-structured data, which stands as a fundamental task in various trending fields like automated math problem solving and neural graph algorithm learning, attracting a lot of research interest. Simultaneously managing both sequential and graph-structured information in such tasks presents a notable challenge. Over recent years, many neural architectures in the literature have emerged to tackle the issue. In this work, we generalize the existing architectures and propose a context-enhanced framework. The crucial innovation is that the reasoning of each step does not only rely on the outcome of the preceding step but also leverages the aggregation of information from more historical outcomes. The idea stems from our observation that in sequential graph reasoning, each step's outcome has a much stronger inner connection with each other compared to traditional seq-to-seq tasks. We show that the framework can effectively integrate with the existing methods, enhancing their reasoning abilities. Empirical evaluations are conducted on the challenging CLRS Reasoning Benchmark, and the results demonstrate that the proposed framework significantly improves the performance of existing architectures, yielding state-of-the-art results across the majority of the datasets within the benchmark.
Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation. Existing studies resort to refining a uniform mapping function to align and correlate the sample regions and subattributes, ignoring two crucial issues: 1) the inherent asymmetry of attributes; and 2) the unutilized channel information. This paper addresses these issues by introducing a simple yet effective approach, dubbed Dual Expert Distillation Network (DEDN), where two experts are dedicated to coarse- and fine-grained visual-attribute modeling, respectively. Concretely, one coarse expert, namely cExp, has a complete perceptual scope to coordinate visual-attribute similarity metrics across dimensions, and moreover, another fine expert, namely fExp, consists of multiple specialized subnetworks, each corresponds to an exclusive set of attributes. Two experts cooperatively distill from each other to reach a mutual agreement during training. Meanwhile, we further equip DEDN with a newly designed backbone network, i.e., Dual Attention Network (DAN), which incorporates both region and channel attention information to fully exploit and leverage visual semantic knowledge. Extensive experiments on various benchmark datasets indicate a new state-of-the-art. The code is available at github.com/zjrao/DEDN.
Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain. To solve this problem, we introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning, and aligns inharmonic graphs with harmonic graphs which close to the source domain for subgraph extraction. In particular, to overcome label scarcity, we employ the spectral seriation algorithm to infer the robust pairwise rankings, which can guide semantic learning using a similarity learning objective. To depict distribution shifts, we utilize spectral clustering and the silhouette coefficient to detect harmonic graphs, which the source model can easily classify. To reduce potential domain discrepancy, we extract domain-invariant subgraphs from inharmonic graphs by an adversarial edge sampling process, which guides the invariant learning of GNNs. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our proposed RNA.
Recently, various deep denoising methods have been proposed to solve the insufficient feature problem in image denoising. These methods can be mainly classified into two categories: (1) Injecting learnable tensors into denoising backbone to supplement feature, which is effective to some extent but may cause serious over-fitting. (2) Using diverse natural images from large image datasets to synthesize noisy images and pre-train denoising models, which can bring model generalization but require large model size and expensive training costs. To address these issues, this paper proposes Implicit Prompt Learning for Image Denoising (IPLID) method to flexibly generate adaptive prompts without meticulously designing them. Specifically, we first introduce an efficient Linear Prompt (LP) block with ultra-few parameters to produce dynamic prompts for both different stages and samples in denoising procedure. We further propose an efficient Compact Feature Fusion (CFF) block to process previous multi-level prompted denoising feature to reconstruct the denoising images. Finally, to further efficiently and effectively produce satisfactory prompt and denoising performance, a Gradient Accumulation (GA) learning scheme is proposed. Experiments on multiple benchmarks showed that the proposed IPLID achieves competitive results with only 1 percent of pre-trained backbone parameters, outperforming classical denoising methods in both efficiency and quality of restored images.
Alleviating Imbalanced Pseudo-label Distribution: Self-Supervised Multi-Source Domain Adaptation with Label-specific Confidence
PDF ↗The existing self-supervised Multi-Source Domain Adaptation (MSDA) methods often suffer an imbalanced characteristic among the distribution of pseudo-labels. Such imbalanced characteristic results in many labels with too many or too few pseudo-labeled samples on the target domain, referred to as easy-to-learn label and hard-to-learn label, respectively. Both of these labels hurt the generalization performance on the target domain. To alleviate this problem, in this paper we propose a novel multi-source domain adaptation method, namely Self-Supervised multi-Source Domain Adaptation with Label-specific Confidence (S3DA-LC). Specifically, we estimate the label-specific confidences, i.e., the learning difficulties of labels, and adopt them to generate the pseudo-labels for target samples, enabling to simultaneously constrain and enrich the pseudo supervised signals for easy-to-learn and hard-to-learn labels. We evaluate S3DA-LC on several benchmark datasets, indicating its superior performance compared with the existing MSDA baselines.
Provable Acceleration of Nesterov’s Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks
PDF ↗Due to its simplicity and efficiency, the first-order gradient method has been extensively employed in training neural networks. Although the optimization problem of the neural network is non-convex, recent research has proved that the first-order method is capable of attaining a global minimum during training over-parameterized neural networks, where the number of parameters is significantly larger than that of training instances. Momentum methods, including the heavy ball (HB) method and Nesterov's accelerated gradient (NAG) method, are the workhorse of first-order gradient methods owning to their accelerated convergence. In practice, NAG often exhibits superior performance than HB. However, current theoretical works fail to distinguish their convergence difference in training neural networks. To fill this gap, we consider the training problem of the two-layer ReLU neural network under over-parameterization and random initialization. Leveraging high-resolution dynamical systems and neural tangent kernel (NTK) theory, our result not only establishes tighter upper bounds of the convergence rate for both HB and NAG, but also provides the first theoretical guarantee for the acceleration of NAG over HB in training neural networks. Finally, we validate our theoretical results on three benchmark datasets.
Anchor based pair-wised multi-view clustering often assumes multi-view data are paired, and has demonstrated significant advancements in recent years. However, this presumption is easily violated, and data is commonly unpaired fully in practical applications due to the influence of data collection and storage processes. Addressing unpaired large-scale multi-view data through anchor learning remains a research gap. The absence of pairing in multi-view data disrupts the consistency and complementarity of multiple views, posing significant challenges in learning powerful and meaningful anchors and bipartite graphs from unpaired multi-view data. To tackle this challenge, this study proposes a novel Fast Unpaired Multi-view Clustering (FUMC) framework for fully unpaired large-scale multi-view data. Specifically, FUMC first designs an inverse local manifold learning paradigm to guide the learned anchors for effective pairing and balancing, ensuring alignment, fairness, and power in unpaired multi-view data. Meanwhile, a novel bipartite graph matching framework is developed to align unpaired bipartite graphs, creating a consistent bipartite graph from unpaired multi-view data. The efficacy, efficiency, and superiority of our FUMC are corroborated through extensive evaluations on numerous benchmark datasets with shallow and deep SOTA methods.
Partial multi-label learning (PMLL) refers to a weakly-supervised classification problem, where each instance is associated with a set of candidate labels, covering its ground-truth labels but also with irrelevant ones. The current methodology of PMLL is to estimate the ground-truth confidences of candidate labels, i.e., the likelihood of a candidate label being a ground-truth one, and induce the multi-label predictor with them, rather than the candidate labels. In this paper, we aim to estimate precise ground-truth confidences by leveraging precise label correlations, which are also required to estimate. To this end, we propose to capture label correlations from both measuring and modeling perspectives. Specifically, we measure the loss between ground-truth confidences and predictions by employing the Wasserstein distance involving label correlations; and form a label correlation-aware regularization to constraint predictive parameters. The two techniques are coupled to promote precise estimations of label correlations. Upon these ideas, we propose a novel PMLL method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (WPML3CP). We conduct extensive experiments on several benchmark datasets. Empirical results demonstrate that WPML3CP can outperform the existing PMLL baselines.
Few-shot learning (FSL) poses a considerable challenge since it aims to improve the model generalization ability with limited labeled data. Previous works usually attempt to construct class-specific prototypes and then predict novel classes using these prototypes. However, the feature distribution represented by the limited labeled data is coarse-grained, leading to large information gap between the labeled and unlabeled data as well as biases in the prototypes. In this paper, we investigate the correlation between sample quality and density, and propose a Density-driven Iterative Prototype Optimization to acquire high-quality prototypes, and further improve few-shot learning performance. Specifically, the proposed method consists of two optimization strategies. The similarity-evaluating strategy is for capturing the information gap between the labeled and unlabeled data by reshaping the feature manifold for the novel feature distribution. The density-driven strategy is proposed to iteratively refine the prototypes in the direction of density growth. The proposed method could reach or even exceed the state-of-the-art performance on four benchmark datasets, including mini-ImageNet, tiered-ImageNet, CUB, and CIFAR-FS. The code will be available soon at https://github.com/tailofcat/DIPO.
Temporal Domain Generalization (TDG) aims at learning models under temporally evolving data distributions and achieving generalization to unseen future data distributions following the evolving trend. Existing advanced TDG methods learn the evolving patterns through the collective behaviors observed at the population-level of instances, such as time-varying statistics and parameters, tending to overlook the impact of individual-level instance evolving processes on the decision boundary. However, a major obstacle is that datasets at different timestamps may comprise unrelated instances and there is no inherent existence of the instance-level evolving trajectories, which hinders us from learning how the decision boundary changes. To address the above challenges, we propose a Continuous-Time modelling Optimal Transport trajectories (CTOT) framework in this paper. Specifically, we utilize optimal transport to align the data distributions between each pair of adjacent source domains to construct instance evolving trajectories. Subsequently, they are modelled by a continuous-time model and extrapolated to generate future virtual instances, which help the model to adapt its decision boundary to the future domain. Extensive experiments on multiple classification and regression benchmarks demonstrate the effectiveness of the proposed CTOT framework. The code and appendix are both available on https://github.com/JinYujie99/CTOT.
Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structure. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. To interpolate between two graphs lying in an irregular domain, EPIC leverages the concept of graph edit distance, constructing an edit path that represents the transformation process between two graphs via edit operations. Moreover, our method introduces a context-sensitive cost model that accounts for the importance of specific edit operations formulated through a learning framework. This allows for a more nuanced transformation process, where the edit distance is not merely count-based but reflects meaningful graph attributes. With randomly sampled graphs from the edit path, we enrich the training set to enhance the generalization capability of classification models. Experimental evaluations across several benchmark datasets demonstrate that our approach outperforms existing augmentation techniques in many tasks.
Offline Reinforcement Learning (RL) aims to learn policies from pre-collected datasets that capture only a subset of the environment's dynamics. The predominant approach has been to solve a constrained optimization formulation, which ensures that the policy visits state-action pairs within the support of the offline dataset. However, this approach has limited the ability to make decisions when the agent faces unknown parts of the environment at deployment time. To address the challenge of decision-making in out-of-support regions, model-based Bayes-adaptive approaches have been proposed by considering all dynamics models that could potentially be the true environment. Since it is generally infeasible to compute the posterior of all dynamics models based on the offline dataset, these approaches usually approximate the posterior by using a finite ensemble of highly probable dynamics models. Hence, the diversity of these models is the key to obtaining good policies. In this work, we propose MoDAP (Model-based Diverse Adaptive Policy Learning), an algorithm to enable the adaptive policy to make informed decisions in previously unexplored states. MoDAP adopts an iterative strategy that simultaneously training the policy and dynamics models. The policy optimization seeks to maximize expected returns across dynamics models, while the dynamics models are trained to promote policy diversification through the proposed information-theoretic objective. We evaluate MoDAP through experiments on the D4RL and NeoRL benchmarks, showcasing its performance superiority over state-of-the-art algorithms.
The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. In response to this issue, researchers have focused on identifying clean samples and reducing the influence of noisy labels. Recent works in this field have achieved notable success in terms of generalizability, albeit at the expense of extensive computing resources. Therefore, reducing computational costs remains a crucial challenge. Concurrently, in other research areas, there has been a focus on developing fine-tuning techniques to efficiently achieve high generalization performance. Despite their proven efficiently achievable generalization capabilities, these techniques have seen limited exploration from a label noise point of view. In this research, we aim to find an effective approach to fine-tune pre-trained models for noisy labeled datasets. To achieve this goal, we empirically investigate the characteristics of pre-trained models on noisy labels and propose an algorithm, named TURN. We present the results of extensive testing and demonstrate both efficient and improved denoising performance on various benchmarks, surpassing previous methods.
Inductive logic reasoning is a fundamental task in graph analysis, which aims to generalize patterns from data. This task has been extensively studied for traditional graph representations, such as knowledge graphs (KGs), using techniques like inductive logic programming (ILP). Existing ILP methods assume learning from KGs with static facts and binary relations. Beyond KGs, graph structures are widely present in other applications such as procedural instructions, scene graphs, and program executions. While ILP is beneficial for these applications, applying it to those graphs is nontrivial: they are more complex than KGs, which usually involve timestamps and n-ary relations, effectively a type of hypergraph with temporal events. In this work, we propose temporal inductive logic reasoning (TILR), an ILP method that reasons on temporal hypergraphs. To enable hypergraph reasoning, we introduce the multi-start random B-walk, a novel graph traversal method for hypergraphs. By combining it with a path-consistency algorithm, TILR learns logic rules by generalizing from both temporal and relational data. To address the lack of hypergraph benchmarks, we create and release two temporal hypergraph datasets: YouCook2-HG and nuScenes-HG. Experiments on these benchmarks demonstrate that TILR achieves superior reasoning capability over various strong baselines.
Regression Residual Reasoning with Pseudo-labeled Contrastive Learning for Uncovering Multiple Complex Compositional Relations
PDF ↗Abstract Visual Reasoning (AVR) has been widely studied in literature. Our study reveals that AVR models tend to rely on appearance matching rather than a genuine understanding of underlying rules. We hence develop a challenging benchmark, Multiple Complex Compositional Reasoning (MC2R), composed of diverse compositional rules on attributes with intentionally increased variations. It aims to identify two outliers from five given images, in contrast to single-answer questions in previous AVR tasks. To solve MC2R tasks, a Regression Residual Reasoning with Pseudo-labeled Contrastive Learning (R3PCL) is proposed, which first transforms the original problem by selecting three images following the same rule, and iteratively regresses one normal image by using the other two, allowing the model to gradually comprehend the underlying rules. The proposed PCL leverages a set of min-max operations to generate more reliable pseudo labels, and exploits contrastive learning with data augmentation on pseudo-labeled images to boost the discrimination and generalization of features. Experimental results on two AVR datasets show that the proposed R3PCL significantly outperforms state-of-the-art models.
The modeling of just noticeable difference (JND) in supervised learning for visual signals has made significant progress. However, existing JND models often suffer from limited generalization due to the need for large-scale training data and their constraints to certain image types. Moreover, these models primarily focus on a single RGB modality, ignoring the potential complementary impacts of multiple modalities. To address these challenges, we propose a new meta-learning approach for the JND modeling, called MetaJND. We introduce two key visual-sensitive modalities like saliency and depth, and leverage a self-attention mechanism for effective interdependence of multi-modal features. Additionally, we incorporate meta-learning for the modality alignment, facilitating dynamic weight generation. Furthermore, we perform hierarchical fusion through multi-layer channel and spatial feature rectification. Experimental results on four benchmark datasets demonstrate the effectiveness of our MetaJND. Moreover, we have also evaluated its performance in compression and watermarking applications, observing higher bit-rate savings and better watermark hiding capabilities.
Efficiently estimating the full-body pose with minimal wearable devices presents a worthwhile research direction. Despite significant advancements in this field, most current research neglects to explore full-body avatar estimation under low-quality signal conditions, which is prevalent in practical usage. To bridge this gap, we summarize three scenarios that may be encountered in real-world applications: standard scenario, instantaneous data-loss scenario, and prolonged data-loss scenario, and propose a new evaluation benchmark. The solution we propose to address data-loss scenarios is integrating the full-body avatar pose estimation problem with motion prediction. Specifically, we present ReliaAvatar, a real-time, reliable avatar animator equipped with predictive modeling capabilities employing a dual-path architecture. ReliaAvatar operates effectively, with an impressive performance rate of 109 frames per second (fps). Extensive comparative evaluations on widely recognized benchmark datasets demonstrate ReliaAvatar's superior performance in both standard and low data-quality conditions. The code is available at https://github.com/MIV-XJTU/ReliaAvatar.
Graph neural networks (GNNs) for graph classification or representation learning require a pooling operation to convert the nodes' embeddings of each graph to a vector as the graph-level representation and the operation has a significant impact on model accuracy. The paper presents a novel graph pooling method called Kernel Readout (KerRead). KerRead maps the node embeddings from the sample space with limited nodes to an augmented sample space with infinite nodes, and then calculates the inner product between some learnable adaptive centers and the augmented node embeddings, which forms a final graph-level feature vector. We apply the proposed strategy to six supervised and two unsupervised graph neural networks such as GCN, GIN, GUNet, InfoGraph, and GraphCL, and the experiments on eight benchmark datasets show that the proposed readout outperforms classical pooling methods such as Sum and seven state-of-the-art pooling methods such as SRead and Janossy GRU. Code and Appendix are both available at https://github.com/jiajunCAU/KerRead.