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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Isometric Quotient Variational Auto-Encoders for Structure-Preserving Representation Learning
Authors: In Huh, changwook jeong, Jae Myung Choe, YOUNGGU KIM, Daesin Kim
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical proof-of-concept experiments reveal that the proposed method can find a meaningful representation of the learned data and outperform other competitors for downstream tasks. [...] We evaluate the model s performance using three datasets: rotated MNIST [44], mixed-type wafer defect maps (Mixed WM38) [42] and cervical cancer cell images (SIPa KMe D) [32]. |
| Researcher Affiliation | Collaboration | In Huh1, , Changwook Jeong2, , Jae Myung Choe1, Young-Gu Kim1, Dae Sin Kim1 1CSE Team, Innovation Center, Samsung Electronics 2Graduate School of Semiconductor Materials and Devices Engineering, UNIST |
| Pseudocode | Yes | Algorithm 1 IQVAEs Input: data {xi}N i=1, hyper-parameters (β, λ), group G, Ginvariant encoders (µG θ , σG θ ), decoder µϕ Initialize θ, ϕ, Cn while training do Sample {αi [0, 1]}N i , {ϵi N(0, I)}N i Compute {µi θ, σi θ}N i = {µG θ (xi) , σG θ (xi)}N i Sample {zi}N i=1 = {µi θ + σi θ ϵi}N i=1 Shuffle {zj}N j=1 = shuffle({zi}N i=1) Augment { zi}N i=1 = {(1 αi)zi + αizj}i=j=N i=j=0 Compute LQAE = PN i=1 ming G g xi µϕ(zi) 2 2 Compute LKL = PN i=1 DKL(N(µi θ, diag[σi θ]2) N(0, In)) Compute {Ji µϕ}N i=1 = {Jµϕ( zi)}N i Compute LISO = PN i=1 (Ji µϕ)TJi µϕ Cn F Optimize (LQAE + βLKL + λLISO)/N w.r.t θ, ϕ end while |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate the model s performance using three datasets: rotated MNIST [44], mixed-type wafer defect maps (Mixed WM38) [42] and cervical cancer cell images (SIPa KMe D) [32]. |
| Dataset Splits | No | The paper specifies training and test sample sizes but does not explicitly provide information about a validation dataset split. |
| Hardware Specification | Yes | We used a single V100 32GB GPU. |
| Software Dependencies | No | The paper mentions using 'optimizer [21]' (referencing Adam) but does not provide specific version numbers for programming languages, machine learning frameworks, or other key software dependencies. |
| Experiment Setup | No | We used the same convolutional architecture3, hyper-parameters, optimizer [21], and training scheme for all models. Implementation details can be found in Section D of SM. |