Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incomplete data, the existing methods mostly attempt to recover data by adding extra terms. However, for the unsupervised methods, a simple recovery strategy will cause errors and outlying value accumulations, which will affect the performance of the methods. Broadly, the previous methods have not taken the effectiveness of recovered instances into consideration, or cannot flexibly balance the discrepancies between recovered data and original data. To address these problems, we propose a novel method termed Manifold-based Incomplete Multi-view clustering via Bi-consistency guidance (MIMB), which flexibly recovers incomplete data among various views, and attempts to achieve biconsistency guidance via reverse regularization. In particular, MIMB adds reconstruction terms to representation learning by recovering missing instances, which dynamically examines the latent consensus representation. Moreover, to preserve the consistency information among multiple views, MIMB implements a biconsistency guidance strategy with reverse regularization of the consensus representation and proposes a manifold embedding measure for exploring the hidden structure of the recovered data. Notably, MIMB aims to balance the importance of different views, and introduces an adaptive weight term for each view. Finally, an optimization algorithm with an alternating iteration optimization strategy is designed for final clustering. Extensive experimental results on 6 benchmark datasets are provided to confirm that MIMB can significantly obtain superior results as compared with several state-of-the-art baselines.

Data-free knowledge distillation (DFKD) improves the student model (S) by mimicking the class probability from a pre-trained teacher model (T) without training data. Under such setting, an ideal scenario is that T can help generate ”good” samples from a generator (G) to maximally benefit S. However, existing arts suffer from the non-ideal generated samples under the disturbance of the gap ( i.e. , either too large or small) between the class probabilities of T and S; for example, the generated samples with too large gap may exhibit excessive information for S, while too small gap leads to the limited knowledge in the samples, resulting into the poor generalization. Meanwhile, they fail to judge the ”goodness” of the generated samples for S since the fixed T is not necessarily ideal. In this paper, we aim to answer what is inside the gap box ; together with how to yield ”good” generated samples for DFKD? To this end, we propose a Gap - S ensitive S ample G eneration (GapSSG) approach, by revisiting the empirical distilled risk from a data-free perspective, which confirms the existence of an ideal teacher (T $^*$ ), while theoretically implying: (1) the gap disturbance originates from the mismatch between T and T $^*$ , hence the class probabilities of T enable the approximation to those of T $^*$ ; and (2) ”good” samples should maximally benefit S via T's class probabilities, owing to unknown T $^*$ . To this end, we unpack the gap box between T and S as two findings: inherent gap to perceive T and T $^*$ ; derived gap to monitor S and T $^*$ . Benefiting from the derived gap that focuses on the adaptability of generated sample to S, we attempt to track student's training route (a series of training epochs) to capture the category distribution of S; upon which, a regulatory factor is further devised to approximate T $^*$ over inherent gap, so as to generate ”good” samples to S. Furthermore, during the distillation process, a sample-balanced strategy comes up to tackle the overfitting and missing knowledge issues between the generated partial and critical samples by training G. The theoretical and empirical studies verify the advantages of GapSSG over the state-of-the-arts. Our code is available at https://github.com/hfutqian/GapSSG .

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel fine-grained text-image fusion based generative adversarial networks (FF-GAN), which consists of two modules: Fine-grained text-image fusion block (FF-Block) and global semantic refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

Graph Contrastive Learning (GCL) has achieved great success in self-supervised representation learning throughout positive and negative pairs based on graph neural networks (GNNs), where one critical issue lies in how to handle the false hard negatives that share the large similarity to the same referenced class as the anchor, which is critical to message passing of GNNs to exploit the graph structure. However, the existing arts either mistakenly identify or miss the false hard negatives, hence resulting into poor node representation. Building on this, there are several crucial bottlenecks — Where do false hard negatives exist upon the anchor? How to well seek false hard negatives? Whether are more false hard negatives better? To answer these questions, in this paper, we propose a novel Locally Weighted Graph Contrastive Learning method, named LocWGCL, while revealing that false hard negatives are primarily distributed in the first-order and second-order neighborhoods of the anchor. Benefiting from the tightness between the first-order nodes and the anchor, representation similarity is calculated to select false hard negatives. For the second-order case, false hard negatives are identified, such that they share the similar passed message with the anchor over the common first-order nodes, along with the large similarity. Upon the seeking process, we devise a weighted strategy to false hard negatives for better node representation. Empirical studies verify the advantages of LocWGCL over the state-of-the-arts on six benchmarks.