Learning Towards The Largest Margins
Authors: Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of our strategy on a variety of tasks, including visual classification, imbalanced classification, person re-identification, and face verification. |
| Researcher Affiliation | Academia | 1Harbin Institute of Technology 2Peng Cheng Laboratory 3King Abdullah University of Science and Technology 4Gaoling School of Artificial Intelligence, Renmin University of China 5Tsinghua University |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states 'All codes are implemented by Py Torch (Paszke et al., 2019)' but does not provide an explicit link or statement for the release of their own source code. |
| Open Datasets | Yes | We conduct experiments of classification on balanced datasets MNIST (Le Cun et al., 1998), CIFAR-10 and CIFAR-100 (Krizhevsky & Hinton, 2009). |
| Dataset Splits | Yes | We follow the controllable data imbalance strategy in (Maas et al., 2011; Cao et al., 2019) to create the imbalanced CIFAR-10/-100 by reducing the number of training examples per class and keeping the validation set unchanged. |
| Hardware Specification | Yes | We use Res Net34 as the feature embedding model and train it on two GPUs NVIDIA Tesla v100 with batch size 512 for all compared methods. |
| Software Dependencies | No | The paper mentions 'All codes are implemented by Py Torch (Paszke et al., 2019)' but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | The number of training epochs is set 100, 200 and 250 for MNIST, CIFAR-10, and CIFAR-100, respectively. For all training, we use SGD optimizer with momentum 0.9 and cosine learning rate annealing (Loshchilov & Hutter, 2016) when Tmax is equal to the corresponding epochs. Weight Decay is set to 1 10 4 for MNIST, CIFAR-10, and CIFAR-100. The initial learning rate is set to 0.01 for MNIST and 0.1 for CIFAR-10 and CIFAR100.Moreover, batch size is set to 256. |