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1805.08974
Cited By
Do Better ImageNet Models Transfer Better?
23 May 2018
Simon Kornblith
Jonathon Shlens
Quoc V. Le
OOD
MLT
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Papers citing
"Do Better ImageNet Models Transfer Better?"
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Title
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Recognizing Instagram Filtered Images with Feature De-stylization
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Ricardo da S. Torres
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Targeted transfer learning to improve performance in small medical physics datasets
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V. Stanković
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Value-laden Disciplinary Shifts in Machine Learning
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Hieu H. Pham
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N. Houlsby
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Self-training with Noisy Student improves ImageNet classification
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Minh-Thang Luong
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On Architectures for Including Visual Information in Neural Language Models for Image Description
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Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Extraction of Complex DNN Models: Real Threat or Boogeyman?
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Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
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A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
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Deep learning tools for the measurement of animal behavior in neuroscience
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Towards Understanding the Transferability of Deep Representations
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Deep Model Transferability from Attribution Maps
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Xiuming Zhang
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Pretraining boosts out-of-domain robustness for pose estimation
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Pretrained AI Models: Performativity, Mobility, and Change
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Saccader: Improving Accuracy of Hard Attention Models for Vision
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SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
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Qingyuan Li
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How Does Learning Rate Decay Help Modern Neural Networks?
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MixConv: Mixed Depthwise Convolutional Kernels
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Natural Adversarial Examples
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Introduction to Camera Pose Estimation with Deep Learning
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FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
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Fixing the train-test resolution discrepancy
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One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
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Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP
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When Does Label Smoothing Help?
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DAWN: Dynamic Adversarial Watermarking of Neural Networks
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