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Found 12 papers, 6 papers with code
Enhancing Real-World Complex Network Representations with Hyperedge Augmentation
These methods cannot fully address the complexities of real-world large-scale networks that often involve higher-order node relations beyond only being pairwise.
Merging Vision Transformers from Different Tasks and Domains
This work targets to merge various Vision Transformers (ViTs) trained on different tasks (i. e., datasets with different object categories) or domains (i. e., datasets with the same categories but different environments) into one unified model, yielding still good performance on each task or domain.
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets.
Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline
Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference.
Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception
Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving.
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Surprisingly, no existing algorithm wins every challenge in MQBench, and we hope this work could inspire future research directions.
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing
However, the inversion process only utilizes biased feature statistics stored in one model and is from low-dimension to high-dimension.
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search
Therefore, we propose to combine Network Architecture Search methods with quantization to enjoy the merits of the two sides.
Once Quantized for All: Progressively Searching for Quantized Compact Models
Automatic search of Quantized Neural Networks (QNN) has attracted a lot of attention.
Balanced Binary Neural Networks with Gated Residual
In this paper, we attempt to maintain the information propagated in the forward process and propose a Balanced Binary Neural Networks with Gated Residual (BBG for short).
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Forward and Backward Information Retention for Accurate Binary Neural Networks
Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks.
Searching for Accurate Binary Neural Architectures
Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices.
