Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Authors: Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 |