2023-07-08 更新
Exploration and Exploitation of Unlabeled Data for Open-Set Semi-Supervised Learning
Authors:Ganlong Zhao, Guanbin Li, Yipeng Qin, Jinjin Zhang, Zhenhua Chai, Xiaolin Wei, Liang Lin, Yizhou Yu
In this paper, we address a complex but practical scenario in semi-supervised learning (SSL) named open-set SSL, where unlabeled data contain both in-distribution (ID) and out-of-distribution (OOD) samples. Unlike previous methods that only consider ID samples to be useful and aim to filter out OOD ones completely during training, we argue that the exploration and exploitation of both ID and OOD samples can benefit SSL. To support our claim, i) we propose a prototype-based clustering and identification algorithm that explores the inherent similarity and difference among samples at feature level and effectively cluster them around several predefined ID and OOD prototypes, thereby enhancing feature learning and facilitating ID/OOD identification; ii) we propose an importance-based sampling method that exploits the difference in importance of each ID and OOD sample to SSL, thereby reducing the sampling bias and improving the training. Our proposed method achieves state-of-the-art in several challenging benchmarks, and improves upon existing SSL methods even when ID samples are totally absent in unlabeled data.
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VoxWatch: An open-set speaker recognition benchmark on VoxCeleb
Authors:Raghuveer Peri, Seyed Omid Sadjadi, Daniel Garcia-Romero
Despite its broad practical applications such as in fraud prevention, open-set speaker identification (OSI) has received less attention in the speaker recognition community compared to speaker verification (SV). OSI deals with determining if a test speech sample belongs to a speaker from a set of pre-enrolled individuals (in-set) or if it is from an out-of-set speaker. In addition to the typical challenges associated with speech variability, OSI is prone to the “false-alarm problem”; as the size of the in-set speaker population (a.k.a watchlist) grows, the out-of-set scores become larger, leading to increased false alarm rates. This is in particular challenging for applications in financial institutions and border security where the watchlist size is typically of the order of several thousand speakers. Therefore, it is important to systematically quantify the false-alarm problem, and develop techniques that alleviate the impact of watchlist size on detection performance. Prior studies on this problem are sparse, and lack a common benchmark for systematic evaluations. In this paper, we present the first public benchmark for OSI, developed using the VoxCeleb dataset. We quantify the effect of the watchlist size and speech duration on the watchlist-based speaker detection task using three strong neural network based systems. In contrast to the findings from prior research, we show that the commonly adopted adaptive score normalization is not guaranteed to improve the performance for this task. On the other hand, we show that score calibration and score fusion, two other commonly used techniques in SV, result in significant improvements in OSI performance.
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Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples
Authors:Shuo He, Lei Feng, Guowu Yang
Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated in complex real-world scenarios, and thus the true label of some collected examples could be unexpectedly outside the assigned candidate label set. In this paper, we term the examples whose true label is outside the candidate label set OOC (out-of-candidate) examples, and pioneer a new PLL study to learn with OOC examples. We consider two types of OOC examples in reality, i.e., the closed-set/open-set OOC examples whose true label is inside/outside the known label space. To solve this new PLL problem, we first calculate the wooden cross-entropy loss from candidate and non-candidate labels respectively, and dynamically differentiate the two types of OOC examples based on specially designed criteria. Then, for closed-set OOC examples, we conduct reversed label disambiguation in the non-candidate label set; for open-set OOC examples, we leverage them for training by utilizing an effective regularization strategy that dynamically assigns random candidate labels from the candidate label set. In this way, the two types of OOC examples can be differentiated and further leveraged for model training. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art PLL methods.
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Multi-Modal Prototypes for Open-Set Semantic Segmentation
Authors:Yuhuan Yang, Chaofan Ma, Chen Ju, Ya Zhang, Yanfeng Wang
In semantic segmentation, adapting a visual system to novel object categories at inference time has always been both valuable and challenging. To enable such generalization, existing methods rely on either providing several support examples as visual cues or class names as textual cues. Through the development is relatively optimistic, these two lines have been studied in isolation, neglecting the complementary intrinsic of low-level visual and high-level language information. In this paper, we define a unified setting termed as open-set semantic segmentation (O3S), which aims to learn seen and unseen semantics from both visual examples and textual names. Our pipeline extracts multi-modal prototypes for segmentation task, by first single modal self-enhancement and aggregation, then multi-modal complementary fusion. To be specific, we aggregate visual features into several tokens as visual prototypes, and enhance the class name with detailed descriptions for textual prototype generation. The two modalities are then fused to generate multi-modal prototypes for final segmentation. On both \pascal and \coco datasets, we conduct extensive experiments to evaluate the framework effectiveness. State-of-the-art results are achieved even on more detailed part-segmentation, Pascal-Animals, by only training on coarse-grained datasets. Thorough ablation studies are performed to dissect each component, both quantitatively and qualitatively.
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