3 research outputs found
Minimizing Supervision in Multi-label Categorization
Multiple categories of objects are present in most images. Treating this as a
multi-class classification is not justified. We treat this as a multi-label
classification problem. In this paper, we further aim to minimize the
supervision required for providing supervision in multi-label classification.
Specifically, we investigate an effective class of approaches that associate a
weak localization with each category either in terms of the bounding box or
segmentation mask. Doing so improves the accuracy of multi-label
categorization. The approach we adopt is one of active learning, i.e.,
incrementally selecting a set of samples that need supervision based on the
current model, obtaining supervision for these samples, retraining the model
with the additional set of supervised samples and proceeding again to select
the next set of samples. A crucial concern is the choice of the set of samples.
In doing so, we provide a novel insight, and no specific measure succeeds in
obtaining a consistently improved selection criterion. We, therefore, provide a
selection criterion that consistently improves the overall baseline criterion
by choosing the top k set of samples for a varied set of criteria. Using this
criterion, we are able to show that we can retain more than 98% of the fully
supervised performance with just 20% of samples (and more than 96% using 10%)
of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach
consistently outperforms all other baseline metrics for all benchmark datasets
and model combinations.Comment: Accepted in CVPR-W 202
Cooperative initialization based deep neural network training
Researchers have proposed various activation functions. These activation
functions help the deep network to learn non-linear behavior with a significant
effect on training dynamics and task performance. The performance of these
activations also depends on the initial state of the weight parameters, i.e.,
different initial state leads to a difference in the performance of a network.
In this paper, we have proposed a cooperative initialization for training the
deep network using ReLU activation function to improve the network performance.
Our approach uses multiple activation functions in the initial few epochs for
the update of all sets of weight parameters while training the network. These
activation functions cooperate to overcome their drawbacks in the update of
weight parameters, which in effect learn better "feature representation" and
boost the network performance later. Cooperative initialization based training
also helps in reducing the overfitting problem and does not increase the number
of parameters, inference (test) time in the final model while improving the
performance. Experiments show that our approach outperforms various baselines
and, at the same time, performs well over various tasks such as classification
and detection. The Top-1 classification accuracy of the model trained using our
approach improves by 2.8% for VGG-16 and 2.1% for ResNet-56 on CIFAR-100
dataset.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
202