High-quality labels are often very scarce, whereas unlabeled data with inferred weak labels occurs more naturally.

This module is trained in an adversarial learning manner [5]. The Selectionmodule selects high possibility candidates from proposals and combines to produce the final prediction, according to the criteria of better position matching.

Neural Information Processing Systems http://nips.cc/

In this paper, we propose AutoPalette, a framework that minimizes color redundancy at the individual image and overall dataset levels, respectively. At the image level, we employ a palette network, a specialized neural network, to dynamically allocate colors from a reduced color space to each pixel. The palette network identifies essential areas in synthetic images for model training and consequently assigns more unique colors to them. At the dataset level, we develop a color-guided initialization strategy to minimize redundancy among images.

Neural Information Processing Systems http://nips.cc/

We contend that utilizing existing relational databases is a promising approach to construct a benchmark that has both merits.

Modern distributed training of machine learning models often suffers from high communication overhead for synchronizing stochastic gradients and model parameters.