Denoising diffusion probabilistic models (DDPMs) for image inpainting aim to add the noise to the texture of the image during the forward process and recover the masked regions with the unmasked ones of the texture via the reverse denoising process. Despite the meaningful semantics gen-eration, the existing arts suffer from the semantic discrep-ancy between the masked and unmasked regions, since the semantically dense unmasked texture fails to be completely degraded while the masked regions turn to the pure noise in diffusion process, leading to the large discrepancy between them. In this paper, we aim to answer how the unmasked se-mantics guide the texture denoising process; together with how to tackle the semantic discrepancy, to facilitate the con-sistent and meaningful semantics generation. To this end, we propose a novel structure-guided diffusion model for image inpainting named StrDiffusion, to reformulate the conventional texture denoising process under the structure guidance to derive a simplified denoising objective for im-age inpainting, while revealing: 1) the semantically sparse structure is beneficial to tackle the semantic discrepancy in the early stage, while the dense texture generates the rea-sonable semantics in the late stage; 2) the semantics from the unmasked regions essentially offer the time-dependent structure guidance for the texture denoising process, ben-efiting from the time-dependent sparsity of the structure semantics. For the denoising process, a structure-guided neural network is trained to estimate the simplified denoising objective by exploiting the consistency of the denoised structure between masked and unmasked regions. Besides, we devise an adaptive resampling strategy as aformal criterion as whether the structure is competent to guide the texture denoising process, while regulate their semantic corre-lations. Extensive experiments validate the merits of StrDif-fusion over the state-of-the-arts. Our code is available at https://github.com/htyjers/StrDiffusion.

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 exhibitexcessiveinformation for S, while too small gap leads to thelimitedknowledge in the samples, resulting into the poor generalization. Meanwhile, they fail to judge the ”goodness” of the generated samples for S since thefixedT is not necessarily ideal. In this paper, we aim to answerwhat is inside the gap box; together withhow to yield ”good” generated samples for DFKD?To this end, we propose aGap-SensitiveSampleGeneration (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 themismatchbetween 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:inherentgap to perceive T and T$^*$;derivedgap to monitor S and T$^*$. Benefiting from thederivedgap 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$^*$overinherentgap, 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 athttps://github.com/hfutqian/GapSSG.

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.

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from full-precision network (P) instead. However, such sample generation process is totally independence of Q, specialized as failing to consider the adaptability of the generated samples, i.e., beneficial or adversarial, over the learning process of Q, resulting into non-ignorable performance loss. Building on this, several crucial questions --- how to measure and exploit the sample adaptability to Q under varied bit-width scenarios? how to generate the samples with desirable adaptability to benefit the quantized network? --- impel us to revisit DFQ. In this paper, we answer the above questions from a game-theory perspective to specialize DFQ as a zero-sum game between two players --- a generator and a quantized network, and further propose an Adaptability-aware Sample Generation (AdaSG) method. Technically, AdaSG reformulates DFQ as a dynamic maximization-vs-minimization game process anchored on the sample adaptability. The maximization process aims to generate the sample with desirable adaptability, such sample adaptability is further reduced by the minimization process after calibrating Q for performance recovery. The Balance Gap is defined to guide the stationarity of the game process to maximally benefit Q. The theoretical analysis and empirical studies verify the superiority of AdaSG over the state-of-the-arts. Our code is available at https://github.com/hfutqian/AdaSG.

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions — how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive Data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players — a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin between two boundaries is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization; 2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://github.com/hfutqian/AdaDFQ.

Deep convolutional neural networks have largely benefited computer vision tasks. However, the high computational complexity limits their real-world applications. To this end, many methods have been proposed for efficient network learning, and applications in portable mobile devices. In this paper, we propose a novel Moving-Mobile-Network, named M2Net, for landmark recognition, equipped each landmark image with located geographic information. We intuitively find that M2Net can essentially promote the diversity of the inference path (selected blocks subset) selection, so as to enhance the recognition accuracy. The above intuition is achieved by our proposed reward function with the input of geo-location and landmarks. We also find that the performance of other portable networks can be improved via our architecture. We construct two landmark image datasets, with each landmark associated with geographic information, over which we conduct extensive experiments to demonstrate that M2Net achieves improved recognition accuracy with comparable complexity.