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..
Adversarial Fisher Vectors for Unsupervised Representation Learning
Authors: Shuangfei Zhai, Walter Talbott, Carlos Guestrin, Joshua Susskind
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments showing that the GAN-induced Fisher Vectors demonstrate competitive performance as unsupervised feature extractors for classification and perceptual similarity tasks. Code is available at https://github.com/apple/ml-afv. |
| Researcher Affiliation | Industry | Shuangfei Zhai Walter Talbott Carlos Guestrin Joshua M. Susskind Apple Inc. EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/apple/ml-afv. |
| Open Datasets | Yes | We conduct our experiments on images of size 32 × 32, using CIFAR10 and CIFAR100 [33], Celeb A [34] and Image Net [35]. |
| Dataset Splits | No | The paper refers to 'validation examples' and 'validation Fisher Similarity' but does not provide specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers and normalization techniques, but it does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | Unless otherwise mentioned, the channel size for convolutional layers is 128. All experiments use batch size 64, ADAM optimizer [37] with β1 = 0, β2 = .999, learning rate for G = 2 × 10−4, and learning rate for D = 4 × 10−4. By default we train our model with a fixed number of iterations (800K) and obtain the last checkpoint for evaluation, unless otherwise mentioned. |