• 제목/요약/키워드: focal loss

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이미지 분류 문제를 위한 focal calibration loss 기반의 지식증류 기법 (Focal Calibration Loss-Based Knowledge Distillation for Image Classification)

  • 강지연 ;이재원 ;이상민
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2023년도 추계학술발표대회
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    • pp.695-697
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    • 2023
  • 최근 몇 년 간 딥러닝 기반 모델의 규모와 복잡성이 증가하면서 강력하고, 높은 정확도가 확보되지만 많은 양의 계산 자원과 메모리가 필요하기 때문에 모바일 장치나 임베디드 시스템과 같은 리소스가 제한된 환경에서의 배포에 제약사항이 생긴다. 복잡한 딥러닝 모델의 배포 및 운영 시 요구되는 고성능 컴퓨터 자원의 문제점을 해결하고자 사전 학습된 대규모 모델로부터 가벼운 모델을 학습시키는 지식증류 기법이 제안되었다. 하지만 현대 딥러닝 기반 모델은 높은 정확도 대비 훈련 데이터에 과적합 되는 과잉 확신(overconfidence) 문제에 대한 대책이 필요하다. 본 논문은 효율적인 경량화를 위한 미리 학습된 모델의 과잉 확신을 방지하고자 초점 손실(focal loss)을 이용한 모델 보정 기법을 언급하며, 다양한 손실 함수 변형에 따라서 지식증류의 성능이 어떻게 변화하는지에 대해 탐구하고자 한다.

Automatic Estimation of Spatially Varying Focal Length for Correcting Distortion in Fisheye Lens Images

  • Kim, Hyungtae;Kim, Daehee;Paik, Joonki
    • IEIE Transactions on Smart Processing and Computing
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    • 제2권6호
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    • pp.339-344
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    • 2013
  • This paper presents an automatic focal length estimation method to correct the fisheye lens distortion in a spatially adaptive manner. The proposed method estimates the focal length of the fisheye lens by generating two reference focal lengths. The distorted fisheye lens image is finally corrected using the orthographic projection model. The experimental results showed that the proposed focal length estimation method is more accurate than existing methods in terms of the loss rate.

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Focal Loss와 앙상블 학습을 이용한 야생조류 소리 분류 기법 (Wild Bird Sound Classification Scheme using Focal Loss and Ensemble Learning)

  • 이재승;유제혁
    • 한국산업정보학회논문지
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    • 제29권2호
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    • pp.15-25
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    • 2024
  • 효과적인 동물 생태계 분석을 위해서는 동물 서식 현황을 자동으로 파악할 수 있는 동물 관제 기술이 중요하다. 특히 울음소리로 종을 판별하는 동물 소리 분류 기술은 영상을 통한 판별이 어려운 환경에서 큰 주목을 받고 있다. 기존 연구들은 단일 딥러닝 모델을 사용하여 동물 소리를 분류하였으나, 야외 환경에서 수집된 동물 소리는 많은 배경 잡음을 포함하여 단일 모델의 판별력을 악화시키며, 종에 따른 데이터 불균형으로 인해 모델의 편향된 학습을 야기한다. 이에, 본 논문에서는 클래스의 데이터 수를 고려하여 페널티를 부여하는 Focal Loss를 사용한 여러 분류 모델의 예측결과를 앙상블을 통해 결합하여 잡음이 많은 동물 소리를 효과적으로 분류할 수 있는 기법을 제안한다. 공개 데이터 셋을 사용한 실험에서, 제안된 기법은 단일 모델의 평균 성능에 비해 Recall 기준으로 최대 22.6%의 성능 개선을 달성하였다.

다발성 신경병증과 재발성 국소 신경병증을 보인 신경성 식욕부진 (Polyneuropathy and Recurrent Focal Neuropathy in Anorexia Nervosa)

  • 김한준;김성훈;이광우
    • Annals of Clinical Neurophysiology
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    • 제3권1호
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    • pp.40-42
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    • 2001
  • Anorexia nervosa(AN) is a disorder characterized by disturbance of body image, fear of gaining weight, severe weight loss and, in female, amenorrhea. Compared with normal persons, patients with AN have neuropathic symptoms more frequently. But electrophysiologic abnormalities have rarely been reported. We experienced a case with recurrent neuropathic symptoms after severe weight loss. Further evaluation revealed AN. Electrophysiologic study showed sensorimotor polyneuropathy and focal neuropathy with conduction block. As far as we know, this feature of neuropathy in AN has not been described. We describe unusual feature of neuropathy in our patient with literature review.

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불균형 블랙박스 동영상 데이터에서 충돌 상황의 다중 분류를 위한 손실 함수 비교 (Comparison of Loss Function for Multi-Class Classification of Collision Events in Imbalanced Black-Box Video Data)

  • 이의상;한석민
    • 한국인터넷방송통신학회논문지
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    • 제24권1호
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    • pp.49-54
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    • 2024
  • 데이터 불균형은 분류 문제에서 흔히 마주치는 문제로, 데이터셋 내의 클래스간 샘플 수의 현저한 차이에서 기인한다. 이러한 데이터 불균형은 일반적으로 분류 모델에서 과적합, 과소적합, 성능 지표의 오해 등의 문제를 야기한다. 이를 해결하기 위한 방법으로는 Resampling, Augmentation, 규제 기법, 손실 함수 조정 등이 있다. 본 논문에서는 손실 함수 조정에 대해 다루며 특히, 불균형 문제를 가진 Multi-Class 블랙박스 동영상 데이터에서 여러 구성의 손실 함수(Cross Entropy, Balanced Cross Entropy, 두 가지 Focal Loss 설정: 𝛼 = 1 및 𝛼 = Balanced, Asymmetric Loss)의 성능을 I3D, R3D_18 모델을 활용하여 비교하였다.

F_MixBERT: Sentiment Analysis Model using Focal Loss for Imbalanced E-commerce Reviews

  • Fengqian Pang;Xi Chen;Letong Li;Xin Xu;Zhiqiang Xing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권2호
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    • pp.263-283
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    • 2024
  • Users' comments after online shopping are critical to product reputation and business improvement. These comments, sometimes known as e-commerce reviews, influence other customers' purchasing decisions. To confront large amounts of e-commerce reviews, automatic analysis based on machine learning and deep learning draws more and more attention. A core task therein is sentiment analysis. However, the e-commerce reviews exhibit the following characteristics: (1) inconsistency between comment content and the star rating; (2) a large number of unlabeled data, i.e., comments without a star rating, and (3) the data imbalance caused by the sparse negative comments. This paper employs Bidirectional Encoder Representation from Transformers (BERT), one of the best natural language processing models, as the base model. According to the above data characteristics, we propose the F_MixBERT framework, to more effectively use inconsistently low-quality and unlabeled data and resolve the problem of data imbalance. In the framework, the proposed MixBERT incorporates the MixMatch approach into BERT's high-dimensional vectors to train the unlabeled and low-quality data with generated pseudo labels. Meanwhile, data imbalance is resolved by Focal loss, which penalizes the contribution of large-scale data and easily-identifiable data to total loss. Comparative experiments demonstrate that the proposed framework outperforms BERT and MixBERT for sentiment analysis of e-commerce comments.

Loss of βPix Causes Defects in Early Embryonic Development, and Cell Spreading and Platelet-Derived Growth Factor-Induced Chemotaxis in Mouse Embryonic Fibroblasts

  • Kang, TaeIn;Lee, Seung Joon;Kwon, Younghee;Park, Dongeun
    • Molecules and Cells
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    • 제42권8호
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    • pp.589-596
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    • 2019
  • ${\beta}Pix$ is a guanine nucleotide exchange factor for the Rho family small GTPases, Rac1 and Cdc42. It is known to regulate focal adhesion dynamics and cell migration. However, the in vivo role of ${\beta}Pix$ is currently not well understood. Here, we report the production and characterization of ${\beta}Pix$-KO mice. Loss of ${\beta}Pix$ results in embryonic lethality accompanied by abnormal developmental features, such as incomplete neural tube closure, impaired axial rotation, and failure of allantois-chorion fusion. We also generated ${\beta}Pix$-KO mouse embryonic fibroblasts (MEFs) to examine ${\beta}Pix$ function in mouse fibroblasts. ${\beta}Pix$-KO MEFs exhibit decreased Rac1 activity, and defects in cell spreading and platelet-derived growth factor (PDGF)-induced ruffle formation and chemotaxis. The average size of focal adhesions is increased in ${\beta}Pix$-KO MEFs. Interestingly, ${\beta}Pix$-KO MEFs showed increased motility in random migration and rapid wound healing with elevated levels of MLC2 phosphorylation. Taken together, our data demonstrate that ${\beta}Pix$ plays essential roles in early embryonic development, cell spreading, and cell migration in fibroblasts.

Puromycin Aminonucleoside 투여로 인한 사구체 족세포의 초미형태학적 변화 (Morphological Changes in Glomerular Podocytes in Puromycin Aminonucleoside Induced Nephropathy)

  • 김영호;박관규;김영만;조수열
    • Applied Microscopy
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    • 제28권4호
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    • pp.577-590
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    • 1998
  • Puromycin aminonucleoside (PAN) nephropathy was induced in a group of Sprague-Dawley rat by a single dose of intraperitoneal Injection to study an ultrastructural change of glomerulus. The experimental rats developed proteinuria three days after PAN injection. Electron microscopic studies of glomeruli showed the loss of epithelial foot processes, formation of cytoplasmic vacuoles, microvillous formation and increased numbers of lysosomes in the cytoplasm of podocytes. It is strongly suggested that proteinuria in PAN nephrosis may be primarily due to a glomerular epithelial lesion, leading to focal disarray of anionic sites or focal defects in the epithelial covering of the basement membrane. The loss of anionic sites in the basement membrane nay be caused by the foot process fusion and the epithelial detachment from the basement membrane.

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Automatic crack detection of dam concrete structures based on deep learning

  • Zongjie Lv;Jinzhang Tian;Yantao Zhu;Yangtao Li
    • Computers and Concrete
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    • 제32권6호
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    • pp.615-623
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    • 2023
  • Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

Computed tomographic features of focal lipogranulomatous lymphangitis for differentiating from malignant intestinal lesions in a dog

  • Hye-Won Lee;Jin-Woo Jung;Seungjo Park;Kija Lee;Sang-Kwon Lee
    • Journal of Veterinary Science
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    • 제24권2호
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    • pp.25.1-25.6
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    • 2023
  • An eight-year-old Maltese dog presented with diarrhea and anorexia. Ultrasonography revealed marked focal wall thickening with loss of layering in the distal ileum. Contrast-enhanced computed tomography (CT) revealed a preserved wall layer with hypoattenuating middle wall thickening. In some segments of the lesion, small nodules protruding toward the mesentery from the outer layer were observed. Histopathology revealed focal lipogranulomatous lymphangitis (FLL) with lymphangiectasia. This is the first report to describe the CT features of FLL in a dog. CT features of preserved wall layers with hypoattenuating middle wall thickening and small nodules can assist in diagnosing FLL in dogs.