Learning Flat Latent Manifolds with VAEs
Authors: Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick Van Der Smagt
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on a range of data-sets, including a videotracking benchmark, where the performance of our unsupervised approach nears that of state-of-the-art supervised approaches, while retaining the computational effciency of straight-line-based ap proaches. |
| Researcher Affiliation | Industry | 1Machine Learning Research Lab, Volkswagen Group, Munich, Germany 2Autonomous Intelligent Driving Gmb H, Munich, Germany. Correspondence to: Nutan Chen <nu tan.chen@gmail.com>. |
| Pseudocode | Yes | The optimisation algorithm Alg. 1, and further details about the optimisation process can be found in App. A.4. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We test our method on artifcial pendulum images, human motion data, MNIST, and MOT16. ... The pendulum data-set (Klushyn et al., 2019; Chen et al., 2018a) consists of 16 x 16-pixel images generated by a pen dulum simulator. ... CMU human motion data set (http://mocap.cs.cmu.edu). ... The binarised MNIST data-set (Larochelle & Murray, 2011) consists of 50,000 training and 10,000 test images... ... MOT16 object-tracking database (Milan et al., 2016)... |
| Dataset Splits | No | The binarised MNIST data-set (Larochelle & Murray, 2011) consists of 50,000 training and 10,000 test images of hand written digits (zero to nine) with 28 x 28 pixels in size. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | To be in line with previous literature (e.g. Higgins et al., 2017; Sønderby et al., 2016), we use the β-parametrisation of the Lagrange multiplier β = 1/λ in our experiments. ... VHP-FMVAE-SORT η = 300 ... VHP-FMVAE-SORT η = 3000 |