Carpe Diem: Seize the Samples Uncertain "at the Moment" for Adaptive Batch Selection

Publication
Song, H., Kim, M., Kim, S., and Lee, J., "Carpe Diem: Seize the Samples Uncertain "at the Moment" for Adaptive Batch Selection," In Proceedings of the 29th ACM International Conference on Information and Knowlege Management (CIKM), October 2020, Ireland. [Paper]

1. Requirement

• Python 3
• tensorflow-gpu 2+
• tensorpack library //use "pip install tensorpack"

2. Description

• This Tutorial is to train DenseNet-25-12 in tensorflow-gpu environment.
• Please do not change the structure of directories:
  • Folder src provides all the code for evaluation with compared methods.
  • Folder src/dataset contains a benchmark dataset (CIFAR-10). Due to the lack of space, the other data will be uploaded soon. Moreover, .bin format is used for the synthetic data because they can be loaded at once in main memory.

3. Dataset

Name	# Training Images	# Testing Images	# Classes	Resolution	Link
CIFAR-10	50,000	10,000	10	32x32	link

4. Tutorial for Evaluation.

• Training Configuration

   # All the hyperparameters of baseline methods were set to the same value described in our paper.
   # Source code provides a tutorial to train DensNet or ResNet using a simple command.

• Necessary Parameters

   - 'gpu_id': gpu number which you want to use (only support single gpu).
   - 'data_name': {CIFAR-10}. # others will be supported later
   - 'method_name': {Online Batch, Active Bias, Recency Bias}.
   - 'optimizer': {sgd, momentum}
   - 'log_dir': log directory to save (1) mini-batch loss/error, (2) training loss/error, and (3) test loss/error.

• Running Command

   python main.py 'gpu_id' 'data_name' 'method_name' 'optimizer' 'log_dir'
   
   # e.g., train on CIFAR-10 using RecencyBias with sgd.
   # python main.py '0' 'CIFAR-10' 'Recency Bias' 'sgd' 'log-cifar-10'

• Detail of Log File

   # convergence_log.csv
   # format: epoch, elapsed time, lr, mini-batch loss, mini-batch error, trainng loss, 
   #         training error, test loss, test error