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Found 17 papers, 8 papers with code
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
The field of image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures.
Parameter-Efficient Masking Networks
We validate the potential of PEMN learning masks on random weights with limited unique values and test its effectiveness for a new compression paradigm based on different network architectures.
S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction
By observing this physical encoding procedure, two major challenges stand in the way of a high-fidelity reconstruction.
Dual Lottery Ticket Hypothesis
In this work, we regard the winning ticket from LTH as the subnetwork which is in trainable condition and its performance as our benchmark, then go from a complementary direction to articulate the Dual Lottery Ticket Hypothesis (DLTH): Randomly selected subnetworks from a randomly initialized dense network can be transformed into a trainable condition and achieve admirable performance compared with LTH -- random tickets in a given lottery pool can be transformed into winning tickets.
Modeling Mask Uncertainty in Hyperspectral Image Reconstruction
Recently, hyperspectral imaging (HSI) has attracted increasing research attention, especially for the ones based on a coded aperture snapshot spectral imaging (CASSI) system.
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION
As the inverse process of snapshot compressive imaging, the hyperspectral image (HSI) reconstruction takes the 2D measurement as input and posteriorly retrieves the captured 3D spatial-spectral signal.
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way.
Automated Graph Learning via Population Based Self-Tuning GCN
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification.
Learning to Mutate with Hypergradient Guided Population
Computing the gradient of model hyperparameters, i. e., hypergradient, enables a promising and natural way to solve the hyperparameter optimization task.
Collaborative Attention Mechanism for Multi-View Action Recognition
Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.
Learnable Subspace Clustering
This paper studies the large-scale subspace clustering (LSSC) problem with million data points.
Generative Partial Multi-View Clustering
Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis.
Automated Relational Meta-learning
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views.
Consensus Clustering: An Embedding Perspective, Extension and Beyond
Consensus clustering fuses diverse basic partitions (i. e., clustering results obtained from conventional clustering methods) into an integrated one, which has attracted increasing attention in both academic and industrial areas due to its robust and effective performance.
Segmentation Guided Image-to-Image Translation with Adversarial Networks
Recently image-to-image translation has received increasing attention, which aims to map images in one domain to another specific one.
Deep Sequential Context Networks for Action Prediction
Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from these partially observed videos.
