Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Robust Disentanglement of a Few Factors at a Time using rPU-VAE
Authors: Benjamin Estermann, Markus Marks, Mehmet Fatih Yanik
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate how the r PU-VAE disentangles different datasets in comparison to the state-ofthe-art. We show how the performance of the r PU-VAE depends on the number of labels generated during training. We train our r PU-VAE approach with 5 random seeds and 3 leaf-runs. We note that the r PU-VAE achieves significantly higher mean disentanglement scores for all datasets used in Fig. 4. |
| Researcher Affiliation | Academia | Benjamin Estermann Institute of Neuroinformatics, ETH Zurich EMAIL Markus Marks Institute of Neuroinformatics, ETH Zurich EMAIL Mehmet Fatih Yanik Institute of Neuroinformatics, ETH Zurich EMAIL |
| Pseudocode | Yes | Algorithm 1 recursive PBT-U-VAE (UDR)(r PU-VAE) 1: procedure DISENTANGLE(D) initial dataset D 2: surrogate Labels empty List() 3: labels0 PBT-U-VAE (UDR)(D) Train until convergence 4: surrogate Labels.append(labels0) 5: for i 1, Max Nr Leaf Runs do Start leaf-runs 6: leaf Labels empty List() 7: d reduce(D, labels0, i) Remove variance of the labeled factor 8: while |d| > sized min no convergence do Recursively label and reduce dataset 9: labelsi PBT-U-VAE (UDR)(d) This is one meta Epoch 10: leaf Labels.append(labelsi) 11: d reduce(d, labelsi, 0) 12: end while 13: surrogate Labels.append(leaf Labels) 14: end for 15: ฮธ PBT-S-VAE (MIG)(D, surrogate Labels) Train ๏ฌnal model 16: return ฮธ 17: end procedure |
| Open Source Code | Yes | Code: https://github.com/besterma/robust_disentanglement |
| Open Datasets | Yes | For evaluation of our approaches in this study, we use the dsprites [6] and shapes3d [27] datasets, as they are some of the most commonly used in the disentanglement literature and therefore enable us to benchmark against other methods [21, 3, 23, 28, 7, 15, 12]. |
| Dataset Splits | Yes | For evaluation of our approaches in this study, we use the dsprites [6] and shapes3d [27] datasets... We compute MIG as well as DCI Disentanglement as described in [7] using disentanglement-lib1. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'disentanglement-lib' but does not specify its version number, nor does it list specific version numbers for other key software components like programming languages, frameworks (e.g., PyTorch, TensorFlow), or other libraries. |
| Experiment Setup | No | The paper discusses hyperparameter sensitivity and tuning generally, and refers to 'Supplementary section B' for how hyperparameters developed, but it does not provide specific hyperparameter values or detailed training configurations (e.g., learning rate, batch size, number of epochs) within the main text provided. |