Diffusion autoencoders (DAs) are variants of diffusion models that use an input-dependent latent variable to capture representations alongside the diffusion process. The latent variable is typically used for tasks such as downstream classification, controllable generation, and interpolation. However, the latent variable has an effect on unconditional generation, with performance strongly dependent on how this variable is modelled and learnt and how it is used in the denoising process. Here, we draw a connection between DAs and a particular class of improved diffusion models---those that learn their forward (noising) process---to argue that DAs have unrealised potential as generative models. We develop a variant of DAs, termed diffusion models with $z$ (DMZ), to show that the adoption of certain design decisions, such as the choice of latent variables and conditioning method, can help realise this generative potential. Through extensive experiments and ablations, we show DMZ converges in fewer iterations, generates high-quality samples with fewer denoising steps, and learns effective latent representations for downstream tasks like classification and domain transfer.
We present CSSL, a contrastive semi-supervised method for medical image segmentation. CSSL samples class-wise patches from labelled images and from confident pseudo-labels, separates their embeddings with a supervised contrastive loss with mixed hard negatives, and uses the Silhouette coefficient of the embedding clusters to set the strength of a feature-perturbation consistency loss. On MoNuSeg, patch embeddings are obtained by masked pooling of the encoder features of the unmasked image, nucleus rims form an additional contrastive class and a memory bank stores past embeddings; on CHAOS, patches are pixel-masked and no memory bank is used. The CSSL losses are added to a weak-to-strong pseudo-labelling recipe. We follow the protocols of published baselines: Self-Loop on MoNuSeg and DAFNet on CHAOS. On MoNuSeg with 50% labels, CSSL reaches 80.87 $\pm$ 0.17 nuclei F1 over three label splits, compared with 79.10 for Self-Loop and 78.73 for the same recipe without CSSL. On CHAOS T2 with 25% labels, CSSL reaches 89.01 Dice, compared with 82.0 for DAFNet and 88.94 for the recipe alone. Ablations show that the contrastive branch and, on MoNuSeg, the boundary class and feature-level pooling account for most of the improvement. Code: https://github.com/prinshul/CSSL
Natural behavior evolves continuously and often reuses movement structure across contexts, but existing behavioral segmentation methods typically assign a single discrete syllable at each time point or impose restrictive generative assumptions. We introduce Motif-based Continuous Dynamics (MCD), a computational framework that models behavior as time-varying combinations of reusable behavioral motifs. MCD learns motifs as latent behavior-level features from transition dynamics, through spectral representation learning within a reinforcement learning framework. MCD then infers policies that combine these motifs across time or context, through offline imitation learning. This formulation avoids hard behavioral boundaries while capturing continuity and compositionality. Across simulated gridworld and continuous-control tasks, mouse maze navigation and pose data, MCD identifies reusable motifs that explain task and temporal variation. MCD therefore provides a flexible and interpretable framework for computational behavior analysis. Code is available in https://github.com/G1NO3/MCD.
Strong data augmentation is a fundamental component of state-of-the-art mean teacher-based Source-Free domain adaptive Object Detection (SFOD) methods, enabling consistency-based self-supervised optimization along weak augmentation. However, our theoretical analysis and empirical observations reveal a critical limitation: strong augmentation can inadvertently erase class-relevant components, leading to artificial inter-category confusion. To address this issue, we introduce Weak-to-strong Semantics Compensation (WSCo), a novel remedy that leverages weakly augmented images, which preserve full semantics, as anchors to enrich the feature space of their strongly augmented counterparts. Essentially, this compensates for the class-relevant semantics that may be lost during strong augmentation on the fly. Notably, WSCo can be implemented as a generic plug-in, easily integrable with any existing SFOD pipelines. Extensive experiments validate the negative impact of strong augmentation on detection performance, and the effectiveness of WSCo in enhancing the performance of previous detection models on standard benchmarks.
Much of the excitement in modern AI is driven by the observation that scaling up existing systems leads to better performance. But does better performance necessarily imply better internal representations? While the representational optimist assumes it must, this position paper challenges that view. We compare neural networks evolved through an open-ended search process to networks trained via conventional stochastic gradient descent (SGD) on the simple task of generating a single image. This minimal setup offers a unique advantage: each hidden neuron's full functional behavior can be easily visualized as an image, thus revealing how the network's output behavior is internally constructed neuron by neuron. The result is striking: while both networks produce the same output behavior, their internal representations differ dramatically. The SGD-trained networks exhibit a form of disorganization that we term fractured entangled representation (FER). Interestingly, the evolved networks largely lack FER, even approaching a unified factored representation (UFR). In large models, FER may be degrading core model capacities like generalization, creativity, and (continual) learning. Therefore, understanding and mitigating FER could be critical to the future of representation learning.
Community detection is a significant challenge in analysing real graphs, including social, citation, biological, and many other networks. This article proposes three new, general, hierarchical frameworks to deal with this task. The introduced approach supports various linkage-based clustering algorithms, vertex proximity matrices, and graph representation learning models. We compare 364 module combinations on Stochastic Block Model graphs and real-life datasets. We observe that our best pipelines (Wasserman-Faust and the mutual information-based PPMI proximity, as well as the deep learning-based DNGR representations) perform competitively with the state-of-the-art Leiden and Louvain algorithms. At the same time, unlike the latter, they remain hierarchical. Thus, they output a series of nested partitions of all possible cardinalities which are compatible with each other. This feature is crucial when the number of correct partitions is unknown in advance, and we show that it is obtained at near-zero practical cost in terms of partition quality. On graphs composed of many small communities, our pipelines are more accurate than the reference solutions. Nevertheless, the downside of our approach is its time complexity, which confines the framework to networks of some 10000 vertices on personal machines.
Recently, feature relation learning has drawn widespread attention in cross-spectral image patch matching. However, existing related research focuses on capturing diverse feature relations between image patches and ignores sufficient intrinsic feature representations of individual image patches. To address this limitation, we propose an innovative relational representation learning that simultaneously focuses on sufficiently mining the intrinsic features of individual image patches and the feature relations between image patches. Based on this, we construct a Relational Representation Learning Network (RRL-Net). Specifically, we innovatively construct an autoencoder to effectively characterize the individual intrinsic features, and introduce a feature interaction learning (FIL) module to extract deep-level feature relations. Meanwhile, to further fully mine individual intrinsic features, a lightweight multi-dimensional global-to-local attention (MGLA) module is constructed to enhance the global feature extraction of individual image patches and capture local dependencies within global features. By combining the MGLA module, we further explore the feature extraction network and construct an attention-based lightweight feature extraction (ALFE) network. Furthermore, a multi-loss post-pruning (MLPP) optimization strategy is proposed, which can greatly facilitate network optimization while avoiding increases in parameters and inference time. Extensive experiments demonstrate that our RRL-Net achieves state-of-the-art (SOTA) performance on multiple public datasets. Our code is available at https://github.com/YuChuang1205/RRL-Net
We present point2vec, a self-supervised method for learning point cloud representations through masked prediction of a teacher network's latent features. Building on data2vec, we investigate a limitation of applying this approach to point clouds: positional information associated with masked patches reveals the overall object shape to the student, weakening the learning task even under heavy masking. point2vec addresses this issue by passing only visible patches to the student encoder and introducing a shallow decoder to predict representations for masked patches. The teacher processes the complete point cloud to provide contextualized training targets. These targets capture global context through the teacher's self-attention, providing supervision beyond the local geometry of individual patches. This design conceals masked-patch positions from the student and reduces its computational cost during pre-training. Our ablations show that the decoder alone provides only part of the improvement; concealing positional information is key. After pre-training on ShapeNet, point2vec outperforms the evaluated self-supervised baselines on shape classification on ModelNet40 and ScanObjectNN and few-shot classification on ModelNet40, while achieving competitive part segmentation results on ShapeNetPart.
PaECTER is an open-source document-level encoder specific for patents. We fine-tune BERT for Patents with examiner-added citation information to generate numerical representations for patent documents. PaECTER performs better in similarity tasks than current state-of-the-art models used in the patent domain. More specifically, our model outperforms the patent specific pre-trained language model (BERT for Patents) and general-purpose text embedding models (e.g., E5, GTE, and BGE) on our patent citation prediction test dataset on different rank evaluation metrics. PaECTER predicts at least one most similar patent at a rank of 1.32 on average when compared against 25 irrelevant patents. Numerical representations generated by PaECTER from patent text can be used for downstream tasks such as classification, tracing knowledge flows, or semantic similarity search. Semantic similarity search is especially relevant in the context of prior art search for both inventors and patent examiners.