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Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems.
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Generative adversarial nets
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Object detectors emerge in deep scene cnns
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Deep learning face attributes in the wild
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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”why should i trust you?” explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Instance normalization: The missing ingredient for fast stylization
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
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Learning deep features for discriminative localization
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Real time image saliency for black box classifiers
P. Dabkowski and Y. Gal · 2017
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
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Concrete dropout
Y. Gal, J. Hron, and A. Kendall · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Deep feature consistent variational autoencoder
X. Hou, L. Shen, K. Sun, and G. Qiu · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Detecting bias with generative counterfactual face attribute augmentation
E. Denton, B. Hutchinson, M. Mitchell, and T. Gebru · 2019
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Cyclical annealing schedule: A simple approach to mitigating kl vanishing
H. Fu, C. Li, X. Liu, J. Gao, A. Celikyilmaz, and L. Carin · 2019
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Counterfactual visual explanations
Y. Goyal, Z. Wu, J. Ernst, D. Batra, D. Parikh, and S. Lee · 2019
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Generative counterfactual introspection for explainable deep learning
S. Liu, B. Kailkhura, D. Loveland, and Y. Han · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Raetsch, S. Gelly, B. Schölkopf, and O. Bachem · 2019
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The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation
S. Jégou, M. Drozdzal, D. Vazquez, A. Romero, and Y. Bengio · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
Cited alongside, same era.
Film: Visual reasoning with a general conditioning layer
E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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Understanding posterior collapse in generative latent variable models
J. Lucas, G. Tucker, R. Grosse, and M. Norouzi · 2019
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Efficient search for diverse coherent explanations
C. Russell · 2019
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Interpretable counterfactual explanations guided by prototypes
A. Van Looveren and J. Klaise · 2019
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Model-agnostic counterfactual explanations for consequential decisions
A.-H. Karimi, G. Barthe, B. Balle, and I. Valera · 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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Synbols: Probing learning algorithms with synthetic datasets
A. Lacoste, P. Rodríguez López, F. Branchaud-Charron, P. Atighehchian, M. Caccia, I. H. Laradji, A. Drouin, M. Craddock, L. Charlin, and D. Vázquez · 2020
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Weakly-supervised disentanglement without compromises
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
R. K. Mothilal, A. Sharma, and C. Tan · 2020
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Learning model-agnostic counterfactual explanations for tabular data
M. Pawelczyk, K. Broelemann, and G. Kasneci · 2020
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Deep structural causal models for tractable counterfactual inference
N. Pawlowski, D. C. Castro, and B. Glocker · 2020
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Face: feasible and actionable counterfactual explanations
R. Poyiadzi, K. Sokol, R. Santos-Rodriguez, T. De Bie, and P. Flach · 2020
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Explanation by progressive exaggeration
S. Singla, B. Pollack, J. Chen, and K. Batmanghelich · 2020
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Counterfactual generative networks
A. Sauer and A. Geiger · 2021
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Conditional generative models for counterfactual explanations
A. Van Looveren, J. Klaise, G. Vacanti, and O. Cobb · 2021
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Generative counterfactuals for neural networks via attribute-informed perturbation
F. Yang, N. Liu, M. Du, and X. Hu · 2021
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