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Found 11 papers, 1 papers with code
MLP Can Be A Good Transformer Learner
We identify that regarding the attention layer in bottom blocks, their subsequent MLP layers, i. e. two feed-forward layers, can elicit the same entropy quantity.
SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS
The SWAP-Score is strongly correlated with ground-truth performance across various search spaces and tasks, outperforming 15 existing training-free metrics on NAS-Bench-101/201/301 and TransNAS-Bench-101.
On the Sparse DAG Structure Learning Based on Adaptive Lasso
Learning the underlying Bayesian Networks (BNs), represented by directed acyclic graphs (DAGs), of the concerned events from purely-observational data is a crucial part of evidential reasoning.
PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search
This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space during the search.
Ranked #16 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions
Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research.
MoParkeR : Multi-objective Parking Recommendation
Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only.
FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing
We quantify the strength of our proposed framework in different cases and compare it to the existing data-driven approaches.
Adaptive Re-ranking of Deep Feature for Person Re-identification
First, we propose a Deep Feature Fusion (DFF) method to exploit the diverse information embedded in a deep feature.
Background Subtraction using Compressed Low-resolution Images
The essential first step involved in almost all the visual tasks is background subtraction with a static camera.
Saliency Preservation in Low-Resolution Grayscale Images
In this study, we explain the biological and computational motivation for LG, and show, through a range of human eye-tracking and computational modeling experiments, that saliency information is preserved in LG images.
