Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Authors: Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically observe significant improvement over existing methods in terms of both disentanglement and data likelihood (reconstruction quality). |
| Researcher Affiliation | Industry | Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan IBM Research AI Yorktown Heights, NY {abhishk,psattig,avinash.bala}@us.ibm.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We evaluate our proposed method, DIP-VAE, on three datasets (i) Celeb A (Liu et al., 2015): It consists of 202, 599 RGB face images of celebrities... (ii) 3D Chairs (Aubry et al., 2014): It consists of 1393 chair CAD models... (iii) 2D Shapes (Matthey et al., 2017): This is a synthetic dataset of binary 2D shapes... |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., "90% for training and 10% for test" for Celeb A, and "First 80%... for training and the rest... for test" for 3D Chairs) but does not mention a separate validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using CNN network architectures and a linear SVM, but it does not specify any software names with version numbers (e.g., specific deep learning frameworks like PyTorch or TensorFlow, or library versions like scikit-learn). |
| Experiment Setup | Yes | Hyperparameters. For the proposed DIP-VAE-I, in all our experiments we vary λod in the set {1, 2, 5, 10, 20, 50, 100, 500} while fixing λd = 10λod for 2D Shapes and 3D Chairs, and λd = 50λod for Celeb A. For DIP-VAE-II, we fix λod = λd for 2D Shapes, and λod = 2λd for Celeb A. Additionally, for DIP-VAE-II we also penalize the ℓ2-norm of third order central moments of qφ(z) with hyperparameter λ3 = 200 for 2D Shapes data (λ3 = 0 for Celeb A). For β-VAE, we experiment with β = {1, 2, 4, 8, 16, 25, 32, 64, 100, 128 , 200, 256} (where β = 1 corresponds to the VAE). We used a batch size of 400 for all 2D Shapes experiments and 100 for all Celeb A experiments. |