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Generalization beyond the training distribution is a core challenge in machine learning.
The need for biases in learning generalizations
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A. Khosla, T. Zhou, T. Malisiewicz, A. A. Efros, and A. Torralba · 2012
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Imagenet classification with deep convolutional neural networks
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E. Sgouritsa, D. Janzing, J. Peters, and B. Schölkopf · 2013
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Glove: Global Vectors for Word Representation
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VQA: Visual Question Answering
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh · 2015
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Causal inference using invariant prediction: identification and confidence intervals
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Domain-adversarial training of neural networks
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh · 2016
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Revisiting visual question answering baselines
A. Jabri, A. Joulin, and L. van der Maaten · 2016
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
J. Johnson, B. Hariharan, L. van der Maaten, L. Fei-Fei, C. L. Zitnick, and R. B. Girshick · 2016
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
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Stacked Attention Networks for Image Question Answering
Z. Yang, X. He, J. Gao, L. Deng, and A. Smola · 2016
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Yin and yang: Balancing and answering binary visual questions
P. Zhang, Y. Goyal, D. Summers-Stay, D. Batra, and D. Parikh · 2016
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Multi-level variational autoencoder: Learning disentangled representations from grouped observations
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Visual Dialog
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh · 2017
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Conditional variance penalties and domain shift robustness
C. Heinze-Deml and N. Meinshausen · 2017
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Adversarial examples for evaluating reading comprehension systems
R. Jia and P. Liang · 2017
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Deeper, broader and artier domain generalization
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales · 2017
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Elements of causal inference
J. Peters, D. Janzing, and B. Schölkopf · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
J. Zhao, T. Wang, M. Yatskar, V. Ordonez, and K.-W. Chang · 2017
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Adversarial regularization for visual question answering: Strengths, shortcomings, and side effects
G. Grand and Y. Belinkov · 2019
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Quantifying and alleviating the language prior problem in visual question answering
Y. Guo, Z. Cheng, L. Nie, Y. Liu, Y. Wang, and M. Kankanhalli · 2019
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Language-conditioned graph networks for relational reasoning
R. Hu, A. Rohrbach, T. Darrell, and K. Saenko · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
D. A. Hudson and C. D. Manning · 2019
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Learning the difference that makes a difference with counterfactually-augmented data
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Don’t just assume; look and answer: Overcoming priors for visual question answering
A. Agrawal, D. Batra, D. Parikh, and A. Kembhavi · 2018
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Bottom-up and top-down attention for image captioning and vqa
P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang · 2018
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Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. Reid, S. Gould, and A. van den Hengel · 2018
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Women also snowboard: Overcoming bias in captioning models
L. A. Hendricks, K. Burns, K. Saenko, T. Darrell, and A. Rohrbach · 2018
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Compositional attention networks for machine reasoning
D. A. Hudson and C. D. Manning · 2018
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Causal discovery using clusters from observational data
S. Pashami, A. Holst, J. Bae, and S. Nowaczyk · 2018
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Overcoming language priors in visual question answering with adversarial regularization
S. Ramakrishnan, A. Agrawal, and S. Lee · 2018
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D. Kaushik, E. Hovy, and Z. C. Lipton · 2019
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Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training
G. Li, N. Duan, Y. Fang, D. Jiang, and M. Zhou · 2019
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Learning rich image region representation for visual question answering
B. Liu, Z. Huang, Z. Zeng, Z. Chen, and J. Fu · 2019
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Simple but effective techniques to reduce biases
R. K. Mahabadi and J. Henderson · 2019
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Lxmert: Learning cross-modality encoder representations from transformers
H. Tan and M. Bansal · 2019
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On incorporating semantic prior knowledge in deep learning through embedding-space constraints
D. Teney, E. Abbasnejad, and A. van den Hengel · 2019
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Actively seeking and learning from live data
D. Teney and A. van den Hengel · 2019
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Learning robust global representations by penalizing local predictive power
H. Wang, S. Ge, Z. Lipton, and E. P. Xing · 2019
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Deep modular co-attention networks for visual question answering
Z. Yu, J. Yu, Y. Cui, D. Tao, and Q. Tian · 2019
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Invariant risk minimization games
K. Ahuja, K. Shanmugam, K. Varshney, and A. Dhurandhar · 2020
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A. R. Akula, S. Gella, Y. Al-Onaizan, S.-C. Zhu, and S. Reddy · 2020
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S. Chang, Y. Zhang, M. Yu, and T. S. Jaakkola · 2020
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An empirical study of invariant risk minimization
Y. J. Choe, J. Ham, and K. Park · 2020
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Environment inference for invariant learning
E. Creager, J.-H. Jacobsen, and R. Zemel · 2020
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In search of lost domain generalization
I. Gulrajani and D. Lopez-Paz · 2020
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Variational autoencoders and nonlinear ica: A unifying framework
I. Khemakhem, D. Kingma, R. Monti, and A. Hyvarinen · 2020
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The pitfalls of simplicity bias in neural networks
H. Shah, K. Tamuly, A. Raghunathan, P. Jain, and P. Netrapalli · 2020
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Disentanglement by nonlinear ica with general incompressible-flow networks (gin)
P. Sorrenson, C. Rother, and U. Köthe · 2020
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Learning what makes a difference from counterfactual examples and gradient supervision
D. Teney, E. Abbasnejad, and A. van den Hengel · 2020
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On the value of out-of-distribution testing: An example of goodhart’s law
D. Teney, K. Kafle, R. Shrestha, E. Abbasnejad, C. Kanan, and A. van den Hengel · 2020
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