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Deep neural networks can be unreliable in the real world especially when they heavily use spurious features for their predictions.
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An image is worth 16x16 words: Transformers for image recognition at scale
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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Inverting visual representations with convolutional networks
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Deep residual learning for image recognition
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Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks
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Causal inference by using invariant prediction: identification and confidence intervals
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”why should i trust you?”: Explaining the predictions of any classifier
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Towards accountable AI: hybrid human-machine analyses for characterizing system failure
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Distributionally robust neural networks
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Emerging properties in self-supervised vision transformers
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A causal framework for distribution generalization
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Convit: Improving vision transformers with soft convolutional inductive biases
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Ai for radiographic covid-19 detection selects shortcuts over signal
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In search of lost domain generalization
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Conditional variance penalties and domain shift robustness
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Out-of-distribution generalization via risk extrapolation (rex)
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Swin transformer: Hierarchical vision transformer using shifted windows
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Domain generalization using causal matching
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Learning transferable visual models from natural language supervision
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The risks of invariant risk minimization
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Understanding failures of deep networks via robust feature extraction
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Training data-efficient image transformers & distillation through attention
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A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes, 2022
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An online learning approach to interpolation and extrapolation in domain generalization
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Salient imagenet: How to discover spurious features in deep learning?
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