Search Results for author: Zhuang Liu

Found 33 papers, 17 papers with code

Test-Time Training for Generalization under Distribution Shifts

no code implementations ICML 2020 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, University of California Moritz Hardt

We introduce a general approach, called test-time training, for improving the performance of predictive models when training and test data come from different distributions.

Image Classification Self-Supervised Learning

A ConvNet for the 2020s

10 code implementations10 Jan 2022 Zhuang Liu, Hanzi Mao, Chao-yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie

The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.

 Ranked #1 on Domain Generalization on ImageNet-Sketch (using extra training data)

Domain Generalization Image Classification +2

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space

no code implementations3 Jan 2022 Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric Xing

This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework that can search such a sub-structure from the original model end-to-end across multiple dimensions, including the input tokens, MHSA and MLP modules with state-of-the-art performance.

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

2 code implementations CVPR 2020 John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun

We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model's robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions.

Instance Segmentation Panoptic Segmentation +1

Confidence Adaptive Anytime Pixel-Level Recognition

no code implementations1 Apr 2021 Zhuang Liu, Trevor Darrell, Evan Shelhamer

We redesign the exits to account for the depth and spatial resolution of the features for each exit.

Image Classification Pose Estimation +1

Contrastive Learning for Recommender System

no code implementations5 Jan 2021 Zhuang Liu, Yunpu Ma, Yuanxin Ouyang, Zhang Xiong

To solve this problem, we propose a graph contrastive learning module for a general recommender system that learns the embeddings in a self-supervised manner and reduces the randomness of message dropout.

Collaborative Filtering Contrastive Learning +2

Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control

1 code implementation ICLR 2021 Zhuang Liu, Xuanlin Li, Bingyi Kang, Trevor Darrell

In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.

Continuous Control

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

3 code implementations ICCV 2021 Yinbo Chen, Zhuang Liu, Huijuan Xu, Trevor Darrell, Xiaolong Wang

The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear.

Few-Shot Learning General Classification

Convolutional Networks with Dense Connectivity

no code implementations8 Jan 2020 Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

Object Recognition

Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network

no code implementations7 Jan 2020 Huiwei Zhou, Zhuang Liu, Shixian Ning, Yunlong Yang, Chengkun Lang, Yingyu Lin, Kun Ma

Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts.

Entity Embeddings

Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction

no code implementations23 Dec 2019 Huiwei Zhou, Chengkun Lang, Zhuang Liu, Shixian Ning, Yingyu Lin, Lei Du

Results: This paper proposes a novel model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior knowledge for CDR extraction.

Entity Embeddings Relation Extraction

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

no code implementations23 Dec 2019 Huiwei Zhou, Yunlong Yang, Shixian Ning, Zhuang Liu, Chengkun Lang, Yingyu Lin, Degen Huang

KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction.

Relation Extraction

Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection

no code implementations11 Dec 2019 Huiwei Zhou, Xuefei Li, Weihong Yao, Zhuang Liu, Shixian Ning, Chengkun Lang, Lei Du

Finally, the selected relation embedding and the context features are concatenated for PPI extraction.

Exploring Simple and Transferable Recognition-Aware Image Processing

1 code implementation21 Oct 2019 Zhuang Liu, Hung-Ju Wang, Tinghui Zhou, Zhiqiang Shen, Bingyi Kang, Evan Shelhamer, Trevor Darrell

Interestingly, the processing model's ability to enhance recognition quality can transfer when evaluated on models of different architectures, recognized categories, tasks and training datasets.

Image Retrieval Recommendation Systems

Regularization Matters in Policy Optimization

2 code implementations21 Oct 2019 Zhuang Liu, Xuanlin Li, Bingyi Kang, Trevor Darrell

In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.

Continuous Control

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

3 code implementations29 Sep 2019 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt

In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.

Image Classification Self-Supervised Learning +2

Test-Time Training for Out-of-Distribution Generalization

no code implementations25 Sep 2019 Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt

We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions.

Image Classification Self-Supervised Learning

DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering

no code implementations WS 2019 Huiwei Zhou, Bizun Lei, Zhe Liu, Zhuang Liu

BioNLP 2019 proposes Question Answering (QA) task, which encourages the use of text mining technology to automatically judge whether a search result is an answer to the medical question.

Question Answering

Few-shot Object Detection via Feature Reweighting

4 code implementations ICCV 2019 Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, Trevor Darrell

The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples.

Few-Shot Learning Few-Shot Object Detection +1

Few Sample Knowledge Distillation for Efficient Network Compression

1 code implementation CVPR 2020 Tianhong Li, Jianguo Li, Zhuang Liu, Chang-Shui Zhang

Deep neural network compression techniques such as pruning and weight tensor decomposition usually require fine-tuning to recover the prediction accuracy when the compression ratio is high.

Knowledge Distillation Network Pruning +2

Rethinking the Value of Network Pruning

2 code implementations ICLR 2019 Zhuang Liu, Ming-Jie Sun, Tinghui Zhou, Gao Huang, Trevor Darrell

Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned "important" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited "important" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm.

Network Pruning Neural Architecture Search

Knowledge Distillation from Few Samples

no code implementations27 Sep 2018 Tianhong Li, Jianguo Li, Zhuang Liu, ChangShui Zhang

Taking the assumption that both "teacher" and "student" have the same feature map sizes at each corresponding block, we add a $1\times 1$ conv-layer at the end of each block in the student-net, and align the block-level outputs between "teacher" and "student" by estimating the parameters of the added layer with limited samples.

Knowledge Distillation

Object Detection from Scratch with Deep Supervision

1 code implementation25 Sep 2018 Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, xiangyang xue

Thus, a better solution to handle these critical problems is to train object detectors from scratch, which motivates our proposed method.

General Classification Object Detection

DSOD: Learning Deeply Supervised Object Detectors from Scratch

4 code implementations ICCV 2017 Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, xiangyang xue

State-of-the-art object objectors rely heavily on the off-the-shelf networks pre-trained on large-scale classification datasets like ImageNet, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks.

General Classification Object Detection

Snapshot Ensembles: Train 1, get M for free

8 code implementations1 Apr 2017 Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger

In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost.

Densely Connected Convolutional Networks

127 code implementations CVPR 2017 Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

Breast Tumour Classification Crowd Counting +5

Deep Networks with Stochastic Depth

15 code implementations30 Mar 2016 Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger

With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4. 91% on CIFAR-10).

Image Classification

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