14 research outputs found
Accuracy Booster: Performance Boosting using Feature Map Re-calibration
Convolution Neural Networks (CNN) have been extremely successful in solving
intensive computer vision tasks. The convolutional filters used in CNNs have
played a major role in this success, by extracting useful features from the
inputs. Recently researchers have tried to boost the performance of CNNs by
re-calibrating the feature maps produced by these filters, e.g.,
Squeeze-and-Excitation Networks (SENets). These approaches have achieved better
performance by Exciting up the important channels or feature maps while
diminishing the rest. However, in the process, architectural complexity has
increased. We propose an architectural block that introduces much lower
complexity than the existing methods of CNN performance boosting while
performing significantly better than them. We carry out experiments on the
CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can
challenge the state-of-the-art results. Our method boosts the ResNet-50
architecture to perform comparably to the ResNet-152 architecture, which is a
three times deeper network, on classification. We also show experimentally that
our method is not limited to classification but also generalizes well to other
tasks such as object detection.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
202
CPWC:Contextual Point Wise Convolution for Object Recognition
Convolutional layers are a major driving force behind the successes of deep
learning. Pointwise convolution (PWC) is a 1x1 convolutional filter that is
primarily used for parameter reduction. However, the PWC ignores the spatial
information around the points it is processing. This design is by choice, in
order to reduce the overall parameters and computations. However, we
hypothesize that this shortcoming of PWC has a significant impact on the
network performance. We propose an alternative design for pointwise
convolution, which uses spatial information from the input efficiently. Our
design significantly improves the performance of the networks without
substantially increasing the number of parameters and computations. We
experimentally show that our design results in significant improvement in the
performance of the network for classification as well as detection.Comment: Accepted in ICASSP 202
Rectification-based Knowledge Retention for Task Incremental Learning
In the task incremental learning problem, deep learning models suffer from catastrophic forgetting of previously seen classes/tasks as they are trained on new classes/tasks. This problem becomes even harder when some of the test classes do not belong to the training class set, i.e., the task incremental generalized zero-shot learning problem. We propose a novel approach to address the task incremental learning problem for both the non zero-shot and zero-shot settings. Our proposed approach, called Rectification-based Knowledge Retention (RKR), applies weight rectifications and affine transformations for adapting the model to any task. During testing, our approach can use the task label information (task-aware) to quickly adapt the network to that task. We also extend our approach to make it task-agnostic so that it can work even when the task label information is not available during testing. Specifically, given a continuum of test data, our approach predicts the task and quickly adapts the network to the predicted task. We experimentally show that our proposed approach achieves state-of-the-art results on several benchmark datasets for both non zero-shot and zero-shot task incremental learning.</p