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Guidance Not Obstruction: A Conjugate Consistent Enhanced Strategy for Domain Generalization

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arXiv 2024年

作者： Cao, Meng Chen, Songcan The MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing210016 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China

Domain generalization addresses domain shift in real-world applications. Most approaches adopt a domain angle, seeking invariant representation across domains by aligning their marginal distributions, irrespective of individual classes, naturally leading to insufficient exploration of discriminative information. Switching to a class angle, we find that multiple domain-related peaks or clusters within the same individual classes must emerge due to distribution shift. In other words, marginal alignment does not guarantee conditional alignment, leading to suboptimal generalization. Therefore, we argue that acquiring discriminative generalization between classes within domains is crucial. In contrast to seeking distribution alignment, we endeavor to safeguard domain-related between-class discrimination. To this end, we devise a novel Conjugate Consistent Enhanced Module, namely Con2EM, based on a distribution over domains, i.e., a meta-distribution. Specifically, we employ a novel distribution-level Universum strategy to generate supplementary diverse domain-related class-conditional distributions, thereby enhancing generalization. This allows us to resample from these generated distributions to provide feedback to the primordial instance-level classifier, further improving its adaptability to the target-agnostic. To ensure generation accuracy, we establish an additional distribution-level classifier to regularize these conditional distributions. Extensive experiments have been conducted to demonstrate its effectiveness and low computational cost compared to SOTAs. Copyright © 2024, The Authors. All rights reserved.

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Open-set label noise can improve robustness against inherent label noise

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arXiv 2021年

作者： Wei, Hongxin Tao, Lue Xie, Renchunzi An, Bo School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT key Laboratory of Pattern Analysis and Machine Intelligence China

Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-set noises. In this paper, we empirically show that open-set noisy labels can be non-toxic and even benefit the robustness against inherent noisy labels. Inspired by the observations, we propose a simple yet effective regularization by introducing Open-set samples with Dynamic Noisy Labels (ODNL) into training. With ODNL, the extra capacity of the neural network can be largely consumed in a way that does not interfere with learning patterns from clean data. Through the lens of SGD noise, we show that the noises induced by our method are random-direction, conflict-free and biased, which may help the model converge to a flat minimum with superior stability and enforce the model to produce conservative predictions on Out-of-Distribution instances. Extensive experimental results on benchmark datasets with various types of noisy labels demonstrate that the proposed method not only enhances the performance of many existing robust algorithms but also achieves significant improvement on Out-of-Distribution detection tasks even in the label noise setting. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

Deep robust multilevel semantic cross-modal hashing

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arXiv 2020年

作者： Song, Ge Zhao, Jun Tan, Xiaoyang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Miit Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanyang Technological University

Hashing based cross-modal retrieval has rec made significant progress. But straightfor embedding data from different modalities in joint Hamming space will inevitably produce codes due to the intrinsic modality discrep and noises. We present a novel Robust M level Semantic Hashing (RMSH) for more a rate cross-modal retrieval. It seeks to pres fine-grained similarity among data with rich mantics, while explicitly require distances bet dissimilar points to be larger than a specific v for strong robustness. For this, we give an fective bound of this value based on the information coding-theoretic analysis, and the above goals are embodied into a margin-adaptive triplet loss. Furthermore, we introduce pseudo-codes via fusing multiple hash codes to explore seldom-seen semantics, alleviating the sparsity problem of similarity information. Experiments on three benchmarks show the validity of the derived bounds, and our method achieves state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

Open-set label noise can improve robustness against inherent label noise 21

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Open-set label noise can improve robustness against inherent...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Hongxin Wei Lue Tao Renchunzi Xie Bo An School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China and MIIT key Laboratory of Pattern Analysis and Machine Intelligence China

ISBN: (纸本)9781713845393

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PIE: Physics-inspired Low-light Enhancement

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arXiv 2024年

作者： Liang, Dong Xu, Zhengyan Li, Ling Wei, Mingqiang Chen, Songcan MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing Universily of Aeronautics Astronautics Shenzhen Research Institute China

In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE. PIE primarily addresses three issues: (i) To resolve the problem of existing learning-based methods often training a LLE model with strict pixel-correspondence image pairs, we eliminate the need for pixel-correspondence paired training data and instead train with unpaired images. (ii) To address the disregard for negative samples and the inadequacy of their generation in existing methods, we incorporate physics-inspired contrastive learning for LLE and design the Bag of Curves (BoC) method to generate more reasonable negative samples that closely adhere to the underlying physical imaging principle. (iii) To overcome the reliance on semantic ground truths in existing methods, we propose an unsupervised regional segmentation module, ensuring regional brightness consistency while eliminating the dependency on semantic ground truths. Overall, the proposed PIE can effectively learn from unpaired positive/negative samples and smoothly realize non-semantic regional enhancement, which is clearly different from existing LLE efforts. Besides the novel architecture of PIE, we explore the gain of PIE on downstream tasks such as semantic segmentation and face detection. Training on readily available open data and extensive experiments demonstrate that our method surpasses the state-of-the-art LLE models over six independent cross-scenes datasets. PIE runs fast with reasonable GFLOPs in test time, making it easy to use on mobile devices. Code available. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Relative Difficulty Distillation for Semantic Segmentation

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arXiv 2024年

作者： Liang, Dong Sun, Yue Du, Yun Chen, Songcan Huang, Sheng-Jun MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Nanjing University of Aeronautics Astronautics Shenzhen Research Institute China

Current knowledge distillation (KD) methods primarily focus on transferring various structured knowledge and designing corresponding optimization goals to encourage the student network to imitate the output of the teacher network. However, introducing too many additional optimization objectives may lead to unstable training, such as gradient conflicts. Moreover, these methods ignored the guidelines of relative learning difficulty between the teacher and student networks. Inspired by human cognitive science, in this paper, we redefine knowledge from a new perspective – the student and teacher networks’ relative difficulty of samples, and propose a pixel-level KD paradigm for semantic segmentation named Relative Difficulty Distillation (RDD). We propose a two-stage RDD framework: Teacher-Full Evaluated RDD (TFE-RDD) and Teacher-Student Evaluated RDD (TSE-RDD). RDD allows the teacher network to provide effective guidance on learning focus without additional optimization goals, thus avoiding adjusting learning weights for multiple losses. Extensive experimental evaluations using a general distillation loss function on popular datasets such as Cityscapes, CamVid, Pascal VOC, and ADE20k demonstrate the effectiveness of RDD against state-of-the-art KD methods. Additionally, our research showcases that RDD can integrate with existing KD methods to improve their upper performance bound. Codes are available at https://***/sunyueue/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

Adaptive Federated Minimax Optimization with Lower Complexities

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arXiv 2022年

作者： Huang, Feihu Wang, Xinrui Li, Junyi Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States

Federated learning is a popular distributed and privacy-preserving learning paradigm in machine learning. Recently, some federated learning algorithms have been proposed to solve the distributed minimax problems. However, these federated minimax algorithms still suffer from high gradient or communication complexity. Meanwhile, few algorithm focuses on using adaptive learning rate to accelerate these algorithms. To fill this gap, in the paper, we study a class of nonconvex minimax optimization, and propose an efficient adaptive federated minimax optimization algorithm (i.e., AdaFGDA) to solve these distributed minimax problems. Specifically, our AdaFGDA builds on the momentum-based variance reduced and local-SGD techniques, and it can flexibly incorporate various adaptive learning rates by using the unified adaptive matrices. Theoretically, we provide a solid convergence analysis framework for our AdaFGDA algorithm under non-i.i.d. setting. Moreover, we prove our AdaFGDA algorithm obtains a lower gradient (i.e., stochastic first-order oracle, SFO) complexity of O (ϵ-3) with lower communication complexity of O (ϵ-2) in finding ϵ-stationary point of the nonconvex minimax problems. Experimentally, we conduct some experiments on the deep AUC maximization and robust neural network training tasks to verify efficiency of our algorithms. © 2022, CC BY-NC-SA.

关键词： Learning algorithms

Forgetting, Ignorance or Myopia: Revisiting key Challenges in Online Continual Learning

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arXiv 2024年

作者： Wang, Xinrui Geng, Chuanxing Wan, Wenhai Li, Shao-Yuan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China School of Computer Science and Technology Huazhong University of Science and Technology China

Online continual learning (OCL) requires the models to learn from constant, endless streams of data. While significant efforts have been made in this field, most were focused on mitigating the catastrophic forgetting issue to achieve better classification ability, at the cost of a much heavier training workload. They overlooked that in real-world scenarios, e.g., in high-speed data stream environments, data do not pause to accommodate slow models. In this paper, we emphasize that model throughput– defined as the maximum number of training samples that a model can process within a unit of time – is equally important. It directly limits how much data a model can utilize and presents a challenging dilemma for current methods. With this understanding, we revisit key challenges in OCL from both empirical and theoretical perspectives, highlighting two critical issues beyond the well-documented catastrophic forgetting: (i) Model’s ignorance: the single-pass nature of OCL challenges models to learn effective features within constrained training time and storage capacity, leading to a trade-off between effective learning and model throughput;(ii) Model’s myopia: the local learning nature of OCL on the current task leads the model to adopt overly simplified, task-specific features and excessively sparse classifier, resulting in the gap between the optimal solution for the current task and the global objective. To tackle these issues, we propose the Non-sparse Classifier Evolution framework (NsCE) to facilitate effective global discriminative feature learning with minimal time cost. NsCE integrates non-sparse maximum separation regularization and targeted experience replay techniques with the help of pre-trained models, enabling rapid acquisition of new globally discriminative features. Extensive experiments demonstrate the substantial improvements of our framework in performance, throughput and real-world practicality. © 2024, CC BY.

关键词： Contrastive Learning

Dense Face Detection via High-level Context Mining

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Dense Face Detection via High-level Context Mining

International Conference on Automatic Face and Gesture Recognition

作者： Qixiang Geng Dong Liang Huiyu Zhou Liyan Zhang Han Sun Ningzhong Liu MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Informatics University of Leicester

ISBN: (纸本)9781665431774

The appearance degradation caused by low resolution is the core problem of small face detection. Therefore, a natural approach is to assemble information from the context. This paper focuses on how to use high-level contextual information to improve the abilities of anchor-based detectors to detect dense and degenerate faces. We tap the spatial contextual information on the overall view based on the density map, and propose the prior of face co-occurrence for inferred bounding-boxes coordination. We also propose score-size-specific non-maximum suppression to replace the traditional non-maximum suppression at the end of anchor-based detectors. According to the inferred face boxes' quantity, score and size, the proposed synthetical solution reduces false positives and increases true positives. Our method does not require additional training, which is model-independent and can be embedded into existing face detectors. We also propose a dataset - Crowd Face for face detection, which is full of challenges. We expect to supply enough samples to highlight the difficulties of detecting dense and degenerate faces. We embed our proposed methods into state-of-the-art face detectors on massively benchmarked face datasets. Compared with the prior art on the WIDER FACE hard set, our method increase an Average Precision of 0.1 %-1.3%. On Crowd Face, it increases an Average Precision of 1 % – 6%. Dataset is available on: https://***/QxGeng/Crowd-Face.

关键词： Training Degradation Face recognition Conferences Detectors Gesture recognition Benchmark testing