86 research outputs found
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
- …