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Asynchronous Federated Continual Learning

Asynchronous Federated Continual Learning

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Shenaj, Donald Toldo, Marco Rigon, Alberto Zanuttigh, Pietro University of Padova Italy

ISBN: (纸本)9798350302493

The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and pre-defined order. This is not very realistic in federated learning environments where each client works independently in an asynchronous manner getting data for the different tasks in time-frames and orders totally uncorrelated with the other ones. We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots. We tackle this novel task using prototype-based learning, a representation loss, fractal pre-training, and a modified aggregation policy. Our approach, called FedSpace, effectively tackles this task as shown by the results on the CIFAR-100 dataset using 3 different federated splits with 50, 100, and 500 clients, respectively. The code and federated splits are available at https://***/LTTM/FedSpace. © 2023 ieee.

关键词： computer aided instruction

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Unpaired Face Restoration via Learnable Cross-Quality Shift

Unpaired Face Restoration via Learnable Cross-Quality Shift

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dong, Yangyi Zhang, Xiaoyun Wang, Zhixin Zhang, Ya Chen, Siheng Wang, Yanfeng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face restoration aims to recover high-quality (HQ) face images from low-quality (LQ) ones with various unknown degradations. Unpaired face restoration approaches focus on the adaptation to unseen degradations, which is a more challenging setting. Recently, generative facial priors of StyleGAN are used to improve the restoration capability of paired face restoration methods. For unpaired methods, however, using face priors is a challenge due to the lack of paired supervision. To address this issue, we take advantage of the editing capabilities of StyleGAN's latent code and propose a novel learnable cross-quality shift. The proposed learnable cross-quality shift not only introduces the generative facial priors into the unpaired framework, but also enables the straight-forward addition/subtraction in the latent feature space to achieve quality conversion. Furthermore, we design a two-branch framework with the proposed cross-quality shift to deal with unpaired data and improve the fidelity of restoration. With the unpaired framework, our method can be fine-tuned on images with unseen degradation. Experimental results show that (i) compared to state-of-the-art methods, our method improves performances under moderate and severe degradation situations;and (ii) both the proposed learnable cross-quality shift and the two-branch framework benefit the restoration performance.

关键词： Degradation computer vision Codes Face recognition conferences Image restoration Faces

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Denoising diffusion models for out-of-distribution detection

Denoising diffusion models for out-of-distribution detection

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Graham, Mark S. Pinaya, Walter H. L. Tudosiu, Petru-Daniel Nachev, Parashkev Ourselin, Sebastien Cardoso, M. Jorge King's College London United Kingdom University College London United Kingdom

ISBN: (纸本)9798350302493

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck-such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://***/marksgraham/ddpm-ood. © 2023 ieee.

关键词： computer vision

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Self-Calibrated Efficient Transformer for Lightweight Super-Resolution

Self-Calibrated Efficient Transformer for Lightweight Super-...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zou, Wenbin Ye, Tian Zheng, Weixin Zhang, Yunchen Chen, Liang Wu, Yi Fujian Normal Univ Fujian Prov Key Lab Photon Technol Fuzhou Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China China Design Grp Co Ltd Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, deep learning has been successfully applied to the single-image super-resolution (SISR) with remarkable performance. However, most existing methods focus on building a more complex network with a large number of layers, which can entail heavy computational costs and memory storage. To address this problem, we present a lightweight Self-Calibrated Efficient Transformer (SCET) network to solve this problem. The architecture of SCET mainly consists of the self-calibrated module and efficient transformer block, where the self-calibrated module adopts the pixel attention mechanism to extract image features effectively. To further exploit the contextual information from features, we employ an efficient transformer to help the network obtain similar features over long distances and thus recover sufficient texture details. We provide comprehensive results on different settings of the overall network. Our proposed method achieves more remarkable performance than baseline methods.

关键词： Deep learning Visualization computer vision conferences Superresolution computer architecture Transformers

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Does Interference Exist When Training a Once-For-All Network?

Does Interference Exist When Training a Once-For-All Network...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shipard, Jordan Wiliem, Arnold Fookes, Clinton Queensland Univ Technol Signal Proc Artificial Intelligence & Vis Technol Brisbane Qld Australia Sentient Vis Syst Port Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Once trained, a subnet can be derived from the supernet (both architecture and trained weights) and deployed directly to the target platform with little to no retraining or fine-tuning. To train the subnet population, OFA uses a novel training method called Progressive Shrinking (PS) which is designed to limit the negative impact of interference during training. It is believed that higher interference during training results in lower subnet population accuracies. In this work we take a second look at this interference effect. Surprisingly, we find that interference mitigation strategies do not have a large impact on the overall subnet population performance. Instead, we find the subnet architecture selection bias during training to be a more important aspect. To show this, we propose a simple-yet-effective method called Random Subnet Sampling (RSS), which does not have mitigation on the interference effect. Despite no mitigation, RSS is able to produce a better performing subnet population than PS in four small-to-medium-sized datasets;suggesting that the interference effect does not play a pivotal role in these datasets. Due to its simplicity, RSS provides a 1.9x reduction in training times compared to PS. A 6.1x reduction can also be achieved with a reasonable drop in performance when the number of RSS training epochs are reduced.

关键词： Training computer vision Codes conferences Sociology Neural networks Interference

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Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications

Regression or Classification? Reflection on BP prediction fr...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Schrumpf, Fabian Serdack, Paul Rudi Fuchs, Mirco Leipzig Univ Appl Sci Leipzig Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring. In the recent past, research focused extensively on non-invasive PPG-based approaches to blood pressure (BP) estimation. These approaches can be subdivided into regression and classification methods. The latter assign PPG signals to predefined BP intervals that represent clinically relevant ranges. The former predict systolic (SBP) and diastolic (DBP) BP as continuous variables and are of particular interest to the research community. However, the reported accuracies of BP regression methods vary widely among publications with some authors even questioning the feasibility of PPG-based BP regression altogether. In our work, we compare BP regression and classification approaches. We argue that BP classification might provide diagnostic value that is equivalent to regression in many clinically relevant scenarios while being similar or even superior in terms of performance. We compare several established neural architectures using publicly available PPG data for SBP regression and classification with and without personalization using subject-specific data. We found that classification and regression models perform similar before personalization. However, after personalization, the accuracy of classification based methods outperformed regression approaches. We conclude that BP classification might be preferable over BP regression in certain scenarios where a coarser segmentation of the BP range is sufficient.

关键词： Heart rate Deep learning computer vision conferences Neural networks Estimation computer architecture

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Blueprint Separable Residual Network for Efficient Image Super-Resolution

Blueprint Separable Residual Network for Efficient Image Sup...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Gu, Jinjin Qiao, Yu Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Beijing Peoples R China Univ Macau Taipa Macao Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent advances in single image super-resolution (SISR) have achieved extraordinary performance, but the computational cost is too heavy to apply in edge devices. To alleviate this problem, many novel and effective solutions have been proposed. Convolutional neural network (CNN) with the attention mechanism has attracted increasing attention due to its efficiency and effectiveness. However, there is still redundancy in the convolution operation. In this paper, we propose Blueprint Separable Residual Network (BSRN) containing two efficient designs. One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation. The other is to enhance the model ability by introducing more effective attention modules. The experimental results show that BSRN achieves state-of-the-art performance among existing efficient SR methods. Moreover, a smaller variant of our model BSRN-S won the first place in model complexity track of NTIRE 2022 Efficient SR Challenge. The code is available at https://***/xiaom233/BSRN.

关键词： Performance evaluation Convolution Computational modeling Superresolution Redundancy Complexity theory pattern recognition

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RV-GAN: Recurrent GAN for Unconditional Video Generation

RV-GAN: Recurrent GAN for Unconditional Video Generation

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Inst Technol Dept Comp Sci Engn Visualizat & Percept Lab Madras Tamil Nadu India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generative models aiming to generate content from noise have achieved high-fidelity synthesis for image data. However, obtaining comparable performance in the field of unconditional video generation still remains challenging. In this work, we propose a recurrent GAN architecture to model the high-dimensional video data distribution. Recurrent networks by design are able to generate complex, long sequences in an autoregressive fashion. However, the standard LSTM unit for videos (ConvLSTM) is not ideally suited for the task of unconditional video generation. Therefore, we propose a simple yet effective LSTM variant called as TransConv LSTM (TC-LSTM) by modulating the conventional ConvLSTM to have a transpose convolutional structure in input-to-state transitions. This enables the network to model both spatial and temporal relationships across layers simultaneously inside the TC-LSTM unit. TC-LSTM unit acts as a building block of our generator. Extensive quantitative and qualitative analysis shows that RV-GAN outperforms state-of-the-art methods by a significant margin on Moving MNIST, MUG, Weizmann and UCF101 datasets. Additionally, owing to the recurrent structure, our method is able to generate high-quality videos, up to 2 times longer (32 frames) than training videos at inference time. Further analysis confirms that the proposed architecture is generic and can be easily adapted to other tasks like class-conditional video synthesis and text-to-video synthesis.

关键词： Training Architecture computer architecture Generative adversarial networks Data models Hybrid power systems Generators

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Sparse Multimodal vision Transformer for Weakly Supervised Semantic Segmentation

Sparse Multimodal Vision Transformer for Weakly Supervised S...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Hanna, Joëlle Mommert, Michael Borth, Damian University of St.Gallen AIML Lab School of Computer Science Switzerland

ISBN: (纸本)9798350302493

vision Transformers have proven their versatility and utility for complex computer vision tasks, such as land cover segmentation in remote sensing applications. While performing on par or even outperforming other methods like Convolutional Neural Networks (CNNs), Transformers tend to require even larger datasets with fine-grained annotations (e.g., pixel-level labels for land cover segmentation). To overcome this limitation, we propose a weakly-supervised vision Transformer that leverages image-level labels to learn a semantic segmentation task to reduce the human annotation load. We achieve this by slightly modifying the architecture of the vision Transformer through the use of gating units in each attention head to enforce sparsity during training and thereby retaining only the most meaningful heads. This allows us to directly infer pixel-level labels from image-level labels by post-processing the un-pruned attention heads of the model and refining our predictions by iteratively training a segmentation model with high fidelity. Training and evaluation on the DFC2020 dataset show that our method1 not only generates high-quality segmentation masks using image-level labels, but also performs on par with fully-supervised training relying on pixel-level labels. Finally, our results show that our method is able to perform weakly-supervised semantic segmentation even on small-scale datasets. © 2023 ieee.

关键词： Semantic Segmentation

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UIGR: Unified Interactive Garment Retrieval

UIGR: Unified Interactive Garment Retrieval

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Han, Xiao He, Sen Zhang, Li Song, Yi-Zhe Xiang, Tao Univ Surrey CVSSP Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interactive garment retrieval (IGR) aims to retrieve a target garment image based on a reference garment image along with user feedback on what to change on the reference garment. Two IGR tasks have been studied extensively: text-guided garment retrieval (TGR) and visually compatible garment retrieval (VCR). The user feedback for the former indicates what semantic attributes to change with the garment category preserved, while the category is the only thing to be changed explicitly for the latter, with an implicit requirement on style preservation. Despite the similarity between these two tasks and the practical need for an efficient system tackling both, they have never been unified and modeled jointly. In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR. To this end, we first contribute a large-scale benchmark suited for both problems. We further propose a strong baseline architecture to integrate TGR and VCR in one model. Extensive experiments suggest that unifying two tasks in one framework is not only more efficient by requiring a single model only, it also leads to better performance. Code and datasets are available at GitHub.

关键词： Computational modeling Clothing Semantics computer architecture Benchmark testing Multitasking pattern recognition

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