Compression with Bayesian Implicit Neural Representations

Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image. Based on this view, data can be compressed by overfitting a compact neural network to its functional representation and then encoding the network weights. However, most current solutions for this are inefficient, as quantization to low-bit precision substantially degrades the reconstruction quality. To address this issue, we propose overfitting variational Bayesian neural networks to the data and compressing an approximate posterior weight sample using relative entropy coding instead of quantizing and entropy coding it. This strategy enables direct optimization of the rate-distortion performance by minimizing the $\beta$-ELBO, and target different rate-distortion trade-offs for a given network architecture by adjusting $\beta$. Moreover, we introduce an iterative algorithm for learning prior weight distributions and employ a progressive refinement process for the variational posterior that significantly enhances performance. Experiments show that our method achieves strong performance on image and audio compression while retaining simplicity.Comment: Preprin

Region Normalization for Image Inpainting

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.Comment: Accepted by AAAI-202

Sp1 Is Essential for p16(INK4a) Expression in Human Diploid Fibroblasts during Senescence

BACKGROUND: p16 (INK4a) tumor suppressor protein has been widely proposed to mediate entrance of the cells into the senescent stage. Promoter of p16 (INK4a) gene contains at least five putative GC boxes, named GC-I to V, respectively. Our previous data showed that a potential Sp1 binding site, within the promoter region from −466 to −451, acts as a positive transcription regulatory element. These results led us to examine how Sp1 and/or Sp3 act on these GC boxes during aging in cultured human diploid fibroblasts. METHODOLOGY/PRINCIPAL FINDINGS: Mutagenesis studies revealed that GC-I, II and IV, especially GC-II, are essential for p16 (INK4a) gene expression in senescent cells. Electrophoretic mobility shift assays (EMSA) and ChIP assays demonstrated that both Sp1 and Sp3 bind to these elements and the binding activity is enhanced in senescent cells. Ectopic overexpression of Sp1, but not Sp3, induced the transcription of p16 (INK4a). Both Sp1 RNAi and Mithramycin, a DNA intercalating agent that interferes with Sp1 and Sp3 binding activities, reduced p16 (INK4a) gene expression. In addition, the enhanced binding of Sp1 to p16 (INK4a) promoter during cellular senescence appeared to be the result of increased Sp1 binding affinity, not an alteration in Sp1 protein level. CONCLUSIONS/SIGNIFICANCE: All these results suggest that GC- II is the key site for Sp1 binding and increase of Sp1 binding activity rather than protein levels contributes to the induction of p16 (INK4a) expression during cell aging

Formation and growth mechanism of Cu-rich layer at aluminum/steel friction welding interface

Utilizing friction welding to achieve the high-quality bonding of 2219 aluminum (Al) alloy/304 stainless steel dissimilar materials has promising applications in aerospace and other industrial fields. The formation of the Cu-rich layer is a distinctive feature that distinguishes it from other series Al alloy/steel friction welding joints. Currently, a summary of the relevant literature reveals that there are three main possible formation mechanisms for the Cu-rich layer: diffusion mechanism, liquefaction mechanism and strengthening phase accumulation mechanism. In this paper, these three formation mechanisms are denied experimentally. Meanwhile, it is proposed that the formation and growth mechanism of the Cu-rich layer is strengthening phase precipitation-reaction-reprecipitation mechanism. The correctness of this theory has also been proved by experiments. The proposed model can facilitate the improvement of the friction welding process of dissimilar materials and provide a theoretical basis for the regulation of the metallurgical reaction of the friction welding joint

Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression

Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significantly longer decoding times, which hinders the practical applications of neural image codecs. This issue is especially pronounced when employing an effective yet time-consuming autoregressive context model since it would increase entropy decoding time by orders of magnitude. In this paper, unlike most previous works that pursue optimal RD performance while temporally overlooking the coding complexity, we make a systematical investigation on the rate-distortion-complexity (RDC) optimization in neural image compression. By quantifying the decoding complexity as a factor in the optimization goal, we are now able to precisely control the RDC trade-off and then demonstrate how the rate-distortion performance of neural image codecs could adapt to various complexity demands. Going beyond the investigation of RDC optimization, a variable-complexity neural codec is designed to leverage the spatial dependencies adaptively according to industrial demands, which supports fine-grained complexity adjustment by balancing the RDC tradeoff. By implementing this scheme in a powerful base model, we demonstrate the feasibility and flexibility of RDC optimization for neural image codecs