Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
Authors: Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf, Stefan Bauer
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 6 provides experimental evidence in a standard disentanglement benchmark dataset supporting the need of a robustness based disentanglement criterion. Our extensive experiments on a standard benchmark dataset show that our robustness based validation is able to discover vulnerabilities of deep representations that have been undetected by existing work. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Zurich, Switzerland 2MPI for Intelligent Systems, T ubingen, Germany. |
| Pseudocode | Yes | Algorithm 1 EMPIDA Estimation 1: Input: 2: dataset D = {(x(i), g(i))}i=1,...,N 3: trained encoder E 4: subsets of factors L {1, . . . , K0} and I, J {1, . . . , K} 5: Preprocessing: 6: encode all samples to obtain {z(i) = E(x(i)) : i = 1, . . . , N} 7: estimate p(g(i)) and p(g(i)\ (I[J)) 8i from relative frequencies in D 8: Estimation: 9: find all realizations of GI in D: {g(k)I , k = 1, . . . , NI} 10: partition the dataset according to those realizations: I := {(x, g) 2 D s.t. g I = g(k)I } 11: for k = 1, . . . , NI do 12: estimate mean E[ZL|do(GI g(k)I )] using Eq. (7) and samples D(k)I 13: partition D(k)I according to realizations of GJ: D(k,l)I,J := {(x, g) 2 D(k)I s.t. g J = g(l)J } 14: initialize mpida(k) 0.0 15: for l = 1, . . . , N (k) 16: meanint E[ZL|do(GI g(k)I , GJ g(l)J )] using Eq. (7) and samples D(k,l)I,J for estimation 17: compute pida d(mean, meanint) 18: update mpida(k) max (mpida(k), pida) 19: end for 20: end for 21: Return empida PNI I | |D| mpida(k) |
| Open Source Code | Yes | For future extensions and applications our work is added to the disentanglement lib of Locatello et al. (2018). |
| Open Datasets | Yes | In many benchmark datasets for disentanglement (e.g. dsprites) the observations are obtained noise-free and the dataset contains all possible combinations of generative factors exactly once. |
| Dataset Splits | No | The paper mentions using 'observational data' and 'benchmark datasets' like 'dsprites' but does not specify the exact train/validation/test splits used for their experiments within the provided text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions various VAE implementations (e.g., classic VAE, β-VAE, DIP-VAE) and refers to the 'disentanglement lib of Locatello et al. (2018)' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper states 'we used each method with the parameter settings that were indicated in the original publications (details are given in Appendix D)', but Appendix D is not included in the provided text, so specific experimental setup details are not accessible. |