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Found 17 papers, 11 papers with code
PTQ4SAM: Post-Training Quantization for Segment Anything
We analyze its characteristics from both per-tensor and per-channel perspectives, and propose a Bimodal Integration strategy, which utilizes a mathematically equivalent sign operation to transform the bimodal distribution into a relatively easy-quantized normal distribution offline.
DB-LLM: Accurate Dual-Binarization for Efficient LLMs
Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical deployment.
Repurposing Coal Power Plants into Thermal Energy Storage for Supporting Zero-carbon Data Centers
Coal power plants will need to be phased out and face stranded asset risks under the net-zero energy system transition.
Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector
However they suffer from severe performance degradation when performing full quantization due to overlooking the unique characteristics of regression tasks in object detection.
Towards Accurate Post-Training Quantization for Vision Transformer
We find the main reasons lie in (1) the existing calibration metric is inaccurate in measuring the quantization influence for extremely low-bit representation, and (2) the existing quantization paradigm is unfriendly to the power-law distribution of Softmax.
BiBench: Benchmarking and Analyzing Network Binarization
Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings by minimizing the bit-width.
BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance
We highlight that benefiting from the compact architecture and optimized hardware kernel, BiFSMNv2 can achieve an impressive 25. 1x speedup and 20. 2x storage-saving on edge hardware.
BiBERT: Accurate Fully Binarized BERT
The large pre-trained BERT has achieved remarkable performance on Natural Language Processing (NLP) tasks but is also computation and memory expensive.
BiFSMN: Binary Neural Network for Keyword Spotting
Then, to allow the instant and adaptive accuracy-efficiency trade-offs at runtime, we also propose a Thinnable Binarization Architecture to further liberate the acceleration potential of the binarized network from the topology perspective.
Exploring Endogenous Shift for Cross-Domain Detection: A Large-Scale Benchmark and Perturbation Suppression Network
To handle the endogenous shift, we further introduce the Perturbation Suppression Network (PSN), motivated by the fact that this shift is mainly caused by two types of perturbations: category-dependent and category-independent ones.
Distribution-sensitive Information Retention for Accurate Binary Neural Network
We present a novel Distribution-sensitive Information Retention Network (DIR-Net) that retains the information in the forward and backward propagation by improving internal propagation and introducing external representations.
Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty
In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts.
Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization
We first give a theoretical analysis that the diversity of synthetic samples is crucial for the data-free quantization, while in existing approaches, the synthetic data completely constrained by BN statistics experimentally exhibit severe homogenization at distribution and sample levels.
Multi-Pretext Attention Network for Few-shot Learning with Self-supervision
Self-supervised learning is emerged as an efficient method to utilize unlabeled data.
Diversifying Sample Generation for Accurate Data-Free Quantization
Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model.
Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images
The images are gathered from an airport and these prohibited items are annotated manually by professional inspectors, which can be used as a benchmark for model training and further facilitate future research.
BiPointNet: Binary Neural Network for Point Clouds
To alleviate the resource constraint for real-time point cloud applications that run on edge devices, in this paper we present BiPointNet, the first model binarization approach for efficient deep learning on point clouds.
