Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

By Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, Jianxiong Xiao. 2015

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Citations in the corpus

Density estimation using Real NVP (2016, methodology): “For CelebA and LSUN, the bits per dimension for the validation set was decreasing throughout training, so little overﬁtting is expected.”
Generative Visual Manipulation on the Natural Image Manifold (2016, background): “com (the shoes are roughly centered but not well aligned, and roughly facing left, with frontal to side view); “church outdoor” dataset (126K images) from the LSUN challenge [38]; “outdoor natural” images (150K) from the MIT Places dataset [39]; and two query-based product collections downloaded fro”
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (2015, methodology): “We trained DCGANs on three datasets, Large-scale Scene Understanding (LSUN) (Yu et al., 2015), Imagenet-1k and a newly assembled Faces dataset.”
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015, background): “While we will provide all urls to the research community, note that this data acquisition is quite feasible for most researchers, and has been done in the past for similarly-sized [9] and even larger collection efforts [62, 65].”
Memory Replay GANs: learning to generate images from new categories without forgetting (2018, methodology): “We also evaluated MeRGANs in a more challenging domain and on higher resolution images (64×64 pixels) using four scene categories of the LSUN dataset [28].”
Designing an encoder for StyleGAN image manipulation (2021, methodology): “We additionally provide results on the LSUN [43] Horse, Cats, and Churches datasets.”
A Study of Face Obfuscation in ImageNet (2021, methodology): “Inspired by prior work (Kuznetsova et al., 2018; Yu et al., 2015), we devise a two-stage semi-automatic pipeline that brings the best of both worlds.”
Generic 3D Representation via Pose Estimation and Matching (2016, methodology): “Table 3 provides the results of layout classiﬁcation [64] using NN classiﬁer on our representation compared to AlexNets FC7 and Pool5.”
Mitigating Neural Network Overconfidence with Logit Normalization (2022, methodology): “Here we use LSUN-C and LSUN-R to denote the cropped and resized version of the LSUN dataset respectively. iSUN (Xu et al., 2015) is a large-scale eye tracking dataset, selected from natural scene images of the SUN database (Xiao et al., 2010).”
Adversarial Learning with Local Coordinate Coding (2018, unknown): “To thoroughly evaluate the proposed method, we conduct experiments on a wide variety of benchmark datasets, including MNIST (LeCun et al., 1998), Oxford-102 (Nilsback & Zisserman, 2008), LSUN (Yu et al., 2015) and CelebA (Liu et al., 2015).”
VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty (2022, methodology): “We use Tiny-ImageNet (Le & Yang) and LSUN (Yu et al., 2015) as OoD datasets for CIFAR-100 and ImageNet experiments respectively.”
Generating Diverse Image Datasets with Limited Labeling (2016, background): “A few semi-supervised approaches have been developed that minimize human effort for dataset creation using an active learning framework [35, 9, 7].”
Out-of-distribution Detection with Deep Nearest Neighbors (2022, methodology): “We evaluate the methods on common OOD datasets: Textures (Cimpoi et al., 2014), SVHN (Netzer et al., 2011), Places365 (Zhou et al., 2017), LSUN-C (Yu et al., 2015), and iSUN (Xu et al., 2015).”
Generative Probabilistic Novelty Detection with Adversarial Autoencoders (2018, methodology): “For outliers are used samples from TinyImageNet [50], LSUN [51], and iSUN [52].”