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..
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Authors: Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our model can generate realistic yet diverse examples, leading to substantial improvements on the Image Net benchmark over the state of the art. |
| Researcher Affiliation | Academia | 1Columbia University, 2Nanyang Technological University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper refers to a GitHub link for a ResNet-10 backbone used in experiments (Released on https://github.com/facebookresearch/low-shot-shrink-hallucinate), but this is a third-party component, not the open-source code for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate our method on the real-world benchmark proposed by Hariharan et al. [11]. This is a challenging task because it requires us to learn a large variety of Image Net [18] |
| Dataset Splits | Yes | Following [11], we split the 1000 Image Net classes into four disjoint class sets Ytest b , Ytest n , Yval b , Yval n , which consist of 193, 300, 196, 311 classes respectively. All of our parameter tuning is done on validation splits, while final results are reported using held-out test splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Our implementation is based on Py Torch [34]" but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Our GAN models are trained for 100000 episodes by ADAM [35] with initial learning rate fixed at 0.0001 which anneals by 0.5 every 20000 episodes. We fix the hyper-parameter m = 10 for computing truncated SVD. For loss term contributions, we set λcyc = 5 and λcov = 0.5 for all final objectives. We choose Z = 100 as the dimension of noise vectors for Gb s input, and C = 50 for the Gaussian mixture. We empirically set batch size B = 1000, and Nb = 20 and Kb = 10 for all training |