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Found 7 papers, 0 papers with code
Criticality-Guided Efficient Pruning in Spiking Neural Networks Inspired by Critical Brain Hypothesis
Firstly, we propose a low-cost metric for the criticality in SNNs.
An Efficient Pruning Process with Locality Aware Exploration and Dynamic Graph Editing for Subgraph Matching
Subgraph matching is a NP-complete problem that extracts isomorphic embeddings of a query graph $q$ in a data graph $G$.
Rectifying the Data Bias in Knowledge Distillation
In this paper, we first quantitatively define the uniformity of the sampled data for training, providing a unified view for methods that learn from biased data.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Exploiting Knowledge Distillation for Few-Shot Image Generation
The existing pipeline is first pretraining a source model (which contains a generator and a discriminator) on a large-scale dataset and finetuning it on a target domain with limited samples.
Switchable K-Class Hyperplanes for Noise-Robust Representation Learning
When collaborated with the popular ArcFace on million-level data representation learning, we found that the switchable manner in SKH can effectively eliminate the gradient conflict generated by real-world label noise on a single K-class hyperplane.
Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization
The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications.
Utilizing the Instability in Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective.
