6 citations · 10 across the 17 of their papers we have counts for
5 papers · 1 filter
PanGu-: Enhancing Language Model Architectures via Nonlinearity Compensation
Yunhe Wang, Hanting Chen, Yehui Tang +17
The recent trend of large language models (LLMs) is to increase the scale of both model size (\aka the number of parameters) and dataset to achieve better generative ability, which…
UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity
Jialong Zuo, Hanyu Zhou, Ying Nie +5
Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts…
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models
Ying Nie, Wei He, Kai Han +4
Vision-language pre-training like CLIP has shown promising performance on various downstream tasks such as zero-shot image classification and image-text retrieval. Most of the exis…
Towards Higher Ranks via Adversarial Weight Pruning
Yuchuan Tian, Hanting Chen, Tianyu Guo +2
Convolutional Neural Networks (CNNs) are hard to deploy on edge devices due to its high computation and storage complexities. As a common practice for model compression, network pr…
Data-Free Distillation of Language Model by Text-to-Text Transfer
Zheyuan Bai, Xinduo Liu, Hailin Hu +3
Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the model when original training data is unavailable. Previous works for DFKD in NLP mainly focus on disti…