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Samples with Low Loss Curvature Improve Data Efficiency

Samples with Low Loss Curvature Improve Data Efficiency

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Garg, Isha Roy, Kaushik Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350301298

In this paper, we study the second order properties of the loss of trained deep neural networks with respect to the training data points to understand the curvature of the loss surface in the vicinity of these points. We find that there is an unexpected concentration of samples with very low curvature. We note that these low curvature samples are largely consistent across completely different architectures, and identifiable in the early epochs of training. We show that the curvature relates to the 'cleanliness' of the data points, with low curvatures samples corresponding to clean, higher clarity samples, representative of their category. Alternatively, high curvature samples are often occluded, have conflicting features and visually atypical of their category. Armed with this insight, we introduce SLo-Curves, a novel coreset identification and training algorithm. SLo-curves identifies the samples with low curvatures as being more data-efficient and trains on them with an additional regularizer that penalizes high curvature of the loss surface in their vicinity. We demonstrate the efficacy of SLo-Curves on CIFAR-10 and CIFAR-100 datasets, where it outperforms state of the art coreset selection methods at small coreset sizes by up to 9%. The identified coresets generalize across architectures, and hence can be pre-computed to generate condensed versions of datasets for use in downstream tasks. Code is available at https://***/ishagarg/SLo-Curves.

关键词： Efficient and scalable vision

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Learning to Exploit Temporal Structure for Biomedical vision-Language Processing

Learning to Exploit Temporal Structure for Biomedical Vision...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Bannur, Shruthi Hyland, Stephanie Liu, Qianchu Perez-Garcia, Fernando Ilse, Maximilian Castro, Daniel C. Boecking, Benedikt Sharma, Harshita Bouzid, Kenza Thieme, Anja Schwaighofer, Anton Wetscherek, Maria Lungren, Matthew P. Nori, Aditya Alvarez-Valle, Javier Oktay, Ozan Microsoft Hlth Futures Redmond WA 98052 USA

ISBN: (纸本)9798350301298

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

关键词： cell microscopy Medical and biological vision

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Fair Federated Medical Image Segmentation via Client Contribution Estimation

Fair Federated Medical Image Segmentation via Client Contrib...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Jiang, Meirui Roth, Holger R. Li, Wenqi Yang, Dong Zhao, Can Nath, Vishwesh Xu, Daguang Dou, Qi Xu, Ziyue Chinese Univ Hong Kong Hong Kong Peoples R China NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9798350301298

How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them together, in order to engage and motivate more diverse clients joining FL to derive a high-quality global model. In this work, we propose a novel method to optimize both types of fairness simultaneously. Specifically, we propose to estimate client contribution in gradient and data space. In gradient space, we monitor the gradient direction differences of each client with respect to others. And in data space, we measure the prediction error on client data using an auxiliary model. Based on this contribution estimation, we propose a FL method, federated training via contribution estimation (FedCE), i.e., using estimation as global model aggregation weights. We have theoretically analyzed our method and empirically evaluated it on two real-world medical datasets. The effectiveness of our approach has been validated with significant performance improvements, better collaboration fairness, better performance fairness, and comprehensive analytical studies. Code is available at https://***. io/NVFlare/research/fed-ce

关键词： cell microscopy Medical and biological vision

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Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Local Implicit Normalizing Flow for Arbitrary-Scale Image Su...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yao, Jie-En Tsao, Li-Yuan Lo, Yi-Chen Tseng, Roy Chang, Chia-Che Lee, Chun-Yi Natl Tsing Hua Univ 1 ElsaLab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.

关键词： Low-level vision

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OpenMix: Exploring Outlier Samples for Misclassification Detection

OpenMix: Exploring Outlier Samples for Misclassification Det...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Fei Cheng, Zhen Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Inst Automat MAIS Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9798350301298

Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications. Unfortunately, modern deep neural networks are often overconfident for their erroneous predictions. In this work, we exploit the easily available outlier samples, i.e., unlabeled samples coming from non-target classes, for helping detect misclassification errors. Particularly, we find that the well-known Outlier Exposure, which is powerful in detecting out-of-distribution (OOD) samples from unknown classes, does not provide any gain in identifying misclassification errors. Based on these observations, we propose a novel method called OpenMix, which incorporates open-world knowledge by learning to reject uncertain pseudo-samples generated via outlier transformation. OpenMix significantly improves confidence reliability under various scenarios, establishing a strong and unified framework for detecting both misclassified samples from known classes and OOD samples from unknown classes. The code is publicly available at https://***/Impression2805/OpenMix.

关键词： detection recognition: Categorization retrieval

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Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution

Better "CMOS" Produces Clearer Images: Learning Space-Varian...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Xuhai Zhang, Jiangning Xu, Chao Wang, Yabiao Wang, Chengjie Liu, Yong Zhejiang Univ APRIL Lab Hangzhou Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-variant due to object motion, out-of-focus, etc., resulting in severe performance drop of the advanced SR methods. To address this problem, we firstly introduce two new datasets with out-of-focus blur, i.e., NYUv2-BSR and Cityscapes-BSR, to support further researches of blind SR with space-variant blur. Based on the datasets, we design a novel Cross-MOdal fuSion network (CMOS) that estimate both blur and semantics simultaneously, which leads to improved SR results. It involves a feature Grouping Interactive Attention (GIA) module to make the two modalities interact more effectively and avoid inconsistency. GIA can also be used for the interaction of other features because of the universality of its structure. Qualitative and quantitative experiments compared with state-of-the-art methods on above datasets and real-world images demonstrate the superiority of our method, e.g., obtaining PSNR/SSIM by +1.91./+0.0048. on NYUv2-BSR than MANet.

关键词： Low-level vision

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Reinforcement Learning-Based Black-Box Model Inversion Attacks

Reinforcement Learning-Based Black-Box Model Inversion Attac...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Han, Gyojin Choi, Jaehyun Lee, Haeil Kim, Junmo Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9798350301298

Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model. Recently, white-box model inversion attacks leveraging Generative Adversarial Networks (GANs) to distill knowledge from public datasets have been receiving great attention because of their excellent attack performance. On the other hand, current black-box model inversion attacks that utilize GANs suffer from issues such as being unable to guarantee the completion of the attack process within a predetermined number of query accesses or achieve the same level of performance as white-box attacks. To overcome these limitations, we propose a reinforcement learning-based black-box model inversion attack. We formulate the latent space search as a Markov Decision Process (MDP) problem and solve it with reinforcement learning. Our method utilizes the confidence scores of the generated images to provide rewards to an agent. Finally, the private data can be reconstructed using the latent vectors found by the agent trained in the MDP. The experiment results on various datasets and models demonstrate that our attack successfully recovers the private information of the target model by achieving state-of-the-art attack performance. We emphasize the importance of studies on privacy-preserving machine learning by proposing a more advanced black-box model inversion attack.

关键词： accountability ethics in vision fairness privacy Transparency

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On-the-fly Category Discovery

On-the-fly Category Discovery

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Du, Ruoyi Chang, Dongliang Liang, Kongming Hospedales, Timothy Song, Yi-Zhe Ma, Zhanyu Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Edinburgh Edinburgh Scotland Univ Surrey Guildford England

ISBN: (纸本)9798350301298

Although machines have surpassed humans on visual recognition problems, they are still limited to providing closed-set answers. Unlike machines, humans can cognize novel categories at the first observation. Novel category discovery (NCD) techniques, transferring knowledge from seen categories to distinguish unseen categories, aim to bridge the gap. However, current NCD methods assume a transductive learning and offline inference paradigm, which restricts them to a pre-defined query set and renders them unable to deliver instant feedback. In this paper, we study on-the-fly category discovery (OCD) aimed at making the model instantaneously aware of novel category samples (i.e., enabling inductive learning and streaming inference). We first design a hash coding-based expandable recognition model as a practical baseline. Afterwards, noticing the sensitivity of hash codes to intra-category variance, we further propose a novel Sign-Magnitude dIsentangLEment (SMILE) architecture to alleviate the disturbance it brings. Our experimental results demonstrate the superiority of SMILE against our baseline model and prior art. Our code is available at https://***/PRISCV/On-the-fly-Category-Discovery.

关键词： detection recognition: Categorization retrieval

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2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

2PCNet: Two-Phase Consistency Training for Day-to-Night Unsu...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kennerley, Mikhail Wang, Jian-Gang Veeravalli, Bharadwaj Tan, Robby T. Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9798350301298

Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established student-teacher framework, particularly for small-scale and low-light objects. This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. The network employs high-confidence bounding-box predictions from the teacher in the first phase and appends them to the student's region proposals for the teacher to re-evaluate in the second phase, resulting in a combination of high and low confidence pseudo-labels. The night images and pseudo-labels are scaled-down before being used as input to the student, providing stronger small-scale pseudo-labels. To address errors that arise from low-light regions and other night-related attributes in images, we propose a night-specific augmentation pipeline called NightAug. This pipeline involves applying random augmentations, such as glare, blur, and noise, to daytime images. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-of-the-art methods by 20%, and to supervised models trained directly on the target data. (1)

关键词： detection recognition: Categorization retrieval

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Glocal Energy-based Learning for Few-Shot Open-Set recognition

Glocal Energy-based Learning for Few-Shot Open-Set Recogniti...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Haoyu Pang, Guansong Wang, Peng Zhang, Lei Wei, Wei Zhang, Yanning Northwestern Polytech Univ Xian Peoples R China Singapore Management Univ Singapore Singapore Univ Wollonong Wollongong NSW Australia

ISBN: (纸本)9798350301298

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closed-set classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of open-set samples, our model leverages both class-wise and pixel-wise features to learn a glocal energy-based score, in which a global energy score is learned using the class-wise features, while a local energy score is learned using the pixel-wise features. The model is enforced to assign large energy scores to samples that are deviated from the few-shot examples in either the class-wise features or the pixel-wise features, and to assign small energy scores otherwise. Experiments on three standard FSOR datasets show the superior performance of our model.(1)

关键词： detection recognition: Categorization retrieval

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