{"type":"ScholarlyArticle","title":"ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing","authors":[{"name":"Christoph Reinders","slug":"christoph-reinders","given":"Christoph","family":"Reinders","affiliation":"Institute for Information Processing , Leibniz University Hannover"},{"name":"Frederik Schubert","slug":"frederik-schubert","given":"Frederik","family":"Schubert","affiliation":"Institute for Information Processing , Leibniz University Hannover"},{"name":"Bodo Rosenhahn","slug":"bodo-rosenhahn","given":"Bodo","family":"Rosenhahn","affiliation":"Institute for Information Processing , Leibniz University Hannover"}],"abstract":"Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed architecture called ChimeraMix learns a data augmentation by generating compositions of instances. The generative model encodes images in pairs, combines the features guided by a mask, and creates new samples. For evaluation, all methods are trained from scratch without any additional data. Several experiments on benchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate the superior performance of ChimeraMix compared to current state-of-the-art methods for classification on small datasets. Code is available at https://github.com/creinders/ChimeraMix.","doi":"10.24963/ijcai.2022/181","url":"https://ijcai.org/proceedings/2022/181","pdf":"https://ijcai.org/proceedings/2022/0181.pdf","markdown":"https://ijcai.org/proceedings/2022/181.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/181","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Computer Vision","track":"Main Track","number":181,"pages":{"first":1298,"last":1305},"keywords":["Computer Vision: Machine Learning for Vision","Computer Vision: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: Neural generative models, auto encoders, GANs"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986864","length_sec":67,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985007","length_sec":348,"provider":"slideslive"}],"references":[{"position":1,"raw":"Baseline 31.37 ± 3.28 38.09 ± 1.34 47.50 ± 2.09 53.19 ± 0.60 58.84 ± 0.82 70.34 ± 1.17 Cutout 28.88 ± 2.84 37.33 ± 1.03 47.55 ± 2.06 53.39 ± 1.32 61.17 ± 1.03 72.14 ± 1.10 Random Erasing 28.91 ± 2.64 37.13 ± 0.61 47.20 ± 2.32 53.11 ± 1.65 60.34 ± 0.35 72.00 ± 0.71 Cosine 31.45 ± 3.22 37.88 ± 1.24 46.69 ± 1.38 52.16 ± 0.72 59.24 ± 1.60 70.18 ± 1.32 MixUp 33.41 ± 2.70 43.03 ± 1.21 53.09 ± 1.00 59.47 ± 1.10 66.16 ± 0.78 74.23 ± 0.35 Scattering 30.50 ± 3.87 37.28 ± 1.87 45.65 ± 1.45 50.47 ± 1.19 54.30 ± 0.95 61.51 ± 0.79 GLICO 31.91 ± 2.41 42.02 ± 0.87 51.61 ± 1.23 59.03 ± 0.70 65.00 ± 1.24 73.96 ± 0.81 ChimeraMix+Grid 36.94 ± 2.63 45.57 ± 2.11 53.67 ± 2.84 59.66 ± 1.35 65.42 ± 0.83 73.76 ± 0.30 ChimeraMix+Seg 37.31 ± 2.57 47.60 ± 1.81 56.21 ± 1.77 60.92 ± 0.62 67.30 ± 1.21 74.96 ± 0.21 STL-10 Baseline 27.61 ± 0.90 31.93 ± 1.68 36.50 ± 0.94 39.95 ± 1.26 44.82 ± 0.48 53.51 ± 1.65 Cutout 28.05 ± 1.73 31.45 ± 2.46 37.68 ± 1.30 40.69 ± 1.13 45.63 ± 1.19 54.32 ± 1.01 Random Erasing 27.87 ± 1.36 31.32 ± 0.48 36.91 ± 1.45 40.66 ± 0.84 45.93 ± 1.10 53.31 ± 1.52 Cosine 25.97 ± 0.93 30.37 ± 1.34 35.51 ± 0.95 40.05 ± 1.01 45.51 ± 1.23 53.01 ± 1.09 MixUp 30.06 ± 1.80 35.63 ± 0.85 42.44 ± 1.85 45.00 ± 2.71 49.03 ± 1.34 54.38 ± 2.11 GLICO 26.97 ± 0.98 33.02 ± 1.07 37.88 ± 1.22 42.66 ± 0.66 48.40 ± 0.72 54.82 ± 1.94 ChimeraMix+Grid 32.18 ± 0.90 37.01 ± 0.84 43.19 ± 1.03 48.93 ± 1.34 52.81 ± 1.45 60.04 ± 0.27 ChimeraMix+Seg 31.37 ± 1.72 37.05 ± 1.09 44.74 ± 0.60 49.58 ± 0.49 55.06 ± 1.11 60.44 ± 0.71 ciFAIR-100 Baseline 18.78 ± 0.79 24.53 ± 0.28 39.27 ± 0.31 45.99 ± 0.32 53.40 ± 0.36 61.81 ± 0.41 Cutout 19.25 ± 0.52 27.77 ± 0.39 40.72 ± 0.68 47.78 ± 0.39 55.13 ± 0.30 63.26 ± 0.62 Random Erasing 18.35 ± 0.37 26.09 ± 0.74 38.83 ± 1.01 46.14 ± 0.38 54.26 ± 0.08 63.24 ± 0.50 Cosine 18.04 ± 0.87 23.72 ± 0.35 38.84 ± 0.73 45.83 ± 0.43 53.32 ± 0.11 61.50 ± 0.46 MixUp 20.63 ± 0.16 31.03 ± 0.54 41.58 ± 0.40 47.88 ± 0.45 54.87 ± 0.20 62.49 ± 0.52 Scattering 12.67 ± 0.40 18.25 ± 0.56 26.37 ± 0.63 31.51 ± 0.28 36.49 ± 0.42 48.18 ± 0.33 GLICO 19.32 ± 0.39 28.49 ± 0.60 40.45 ± 0.30 45.90 ± 0.77 53.53 ± 0.19 60.68 ± 0.50 SuperMix 19.23 ± 0.45 26.78 ± 0.20 38.47 ± 0.83 44.69 ± 0.63 53.07 ± 0.13 62.63 ± 0.30 ChimeraMix+Grid 20.24 ± 0.12 31.62 ± 0.82 41.80 ± 0.52 48.10 ± 0.71 54.67 ± 1.01 62.13 ± 0.27 ChimeraMix+Seg 21.09 ± 0.47 32.72 ± 0.60 43.23 ± 0.38 48.83 ± 0.72 55.79 ± 0.21 62.96","title":"Baseline","authors":null,"year":null,"venue":"Baseline","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Arora et al., 2020] Sanjeev Arora, Simon S. 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