Online Filter Clustering and Pruning for Efficient Convnets

Pruning filters is an effective method for accelerating deep neural networks (DNNs), but most existing approaches prune filters on a pre-trained network directly which limits in acceleration. Although each filter has its own effect in DNNs, but if two filters are the same with each other, we could prune one safely. In this paper, we add an extra cluster loss term in the loss function which can force filters in each cluster to be similar online. After training, we keep one filter in each cluster and prune others and fine-tune the pruned network to compensate for the loss. Particularly, the clusters in every layer can be defined firstly which is effective for pruning DNNs within residual blocks. Extensive experiments on CIFAR10 and CIFAR100 benchmarks demonstrate the competitive performance of our proposed filter pruning method.Comment: 5 pages, 4 figure

Grid Jigsaw Representation with CLIP: A New Perspective on Image Clustering

Unsupervised representation learning for image clustering is essential in computer vision. Although the advancement of visual models has improved image clustering with efficient visual representations, challenges still remain. Firstly, these features often lack the ability to represent the internal structure of images, hindering the accurate clustering of visually similar images. Secondly, the existing features tend to lack finer-grained semantic labels, limiting the ability to capture nuanced differences and similarities between images. In this paper, we first introduce Jigsaw based strategy method for image clustering called Grid Jigsaw Representation (GJR) with systematic exposition from pixel to feature in discrepancy against human and computer. We emphasize that this algorithm, which mimics human jigsaw puzzle, can effectively improve the model to distinguish the spatial feature between different samples and enhance the clustering ability. GJR modules are appended to a variety of deep convolutional networks and tested with significant improvements on a wide range of benchmark datasets including CIFAR-10, CIFAR-100/20, STL-10, ImageNet-10 and ImageNetDog-15. On the other hand, convergence efficiency is always an important challenge for unsupervised image clustering. Recently, pretrained representation learning has made great progress and released models can extract mature visual representations. It is obvious that use the pretrained model as feature extractor can speed up the convergence of clustering where our aim is to provide new perspective in image clustering with reasonable resource application and provide new baseline. Further, we innovate pretrain-based Grid Jigsaw Representation (pGJR) with improvement by GJR. The experiment results show the effectiveness on the clustering task with respect to the ACC, NMI and ARI three metrics and super fast convergence speed

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI