Doubly Robust Conditional VAE via Decoder Calibration: An Implicit KL Annealing Approach
Authors: Chuanhui Liu, Xiao Wang
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real-world datasets demonstrate the superior performance of our method across various conditional density estimation tasks, highlighting its significance for accurate and reliable probabilistic modeling. The implementation is publicly available at https://github.com/chuanhuiliu/calibrated_cvae. |
| Researcher Affiliation | Academia | Chuanhui Liu EMAIL Department of Statistics, Purdue University Xiao Wang EMAIL Department of Statistics, Purdue University |
| Pseudocode | Yes | A general pseudo-code can be found in Algorithm 1. |
| Open Source Code | Yes | The implementation is publicly available at https://github.com/chuanhuiliu/calibrated_cvae. |
| Open Datasets | Yes | Experimental results on synthetic and real-world datasets demonstrate the superior performance of our method across various conditional density estimation tasks, highlighting its significance for accurate and reliable probabilistic modeling. The implementation is publicly available at https://github.com/chuanhuiliu/calibrated_cvae. ... we also validate our method on the MNIST (Deng, 2012) and Celeb A (Liu et al., 2018) datasets for conditional image generation and reconstruction in Appendix I & J. ...Finally, we compare the performance of calibrated σ-CVAE with Bayesian conditional normalizing flows (Trippe & Turner, 2018) on 6 UCI datasets, as listed in Table 4. |
| Dataset Splits | Yes | The random train-test split is 75% to 25%. |
| Hardware Specification | Yes | We implemented the proposed method using Py Torch 1.8.2 +cu111 with Python 3.7 on an Ubuntu internal cluster with multiple Nvidia GPUs including A10,A30, A100, A100-40GB, A100-80GB, and V100. |
| Software Dependencies | Yes | We implemented the proposed method using Py Torch 1.8.2 +cu111 with Python 3.7 on an Ubuntu internal cluster with multiple Nvidia GPUs including A10,A30, A100, A100-40GB, A100-80GB, and V100. |
| Experiment Setup | Yes | In this section, we provide evidence and more practical insights into the calibration of σ-CVAE through extensive numerical experiments given a finite training sample. We use the Adam (Kingma, 2014) stochastic gradient descent algorithm for training neural networks. The general learning rate is 0.005, and the convergence threshold is 0.001 in the average loss change. ...In Section 5.1, we used fully connected 4-layer neural networks with a hyperbolic tangent activation function for the encoding and decoding networks. The latent dimension is set to 2, and the width of the hidden layers is [16, 8, 4, 2] and [2, 4, 16, 4], respectively. σ initialized at 1. The batch size is equal to the sample size of the training data. |