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07/01/2023
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Extensive studies have shown that deep learning models are vulnerable to...
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03/06/2023
UniHCP: A Unified Model for Human-Centric Perceptions
Human-centric perceptions (e.g., pose estimation, human parsing, pedestr...
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10/11/2022
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative Learning
Data in real-world object detection often exhibits the long-tailed distr...
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10/11/2022
The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition
Long-tail distribution is widely spread in real-world applications. Due ...
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09/27/2022
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Transformer architecture has become the fundamental element of the wides...
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09/23/2022
Towards Frame Rate Agnostic Multi-Object Tracking
Multi-Object Tracking (MOT) is one of the most fundamental computer visi...
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09/06/2022
Mimose: An Input-Aware Checkpointing Planner for Efficient Training on GPU
Larger deep learning models usually lead to higher model quality with an...
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03/11/2022
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Recently, post-training quantization (PTQ) has driven much attention to ...
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01/07/2022
Equalized Focal Loss for Dense Long-Tailed Object Detection
Despite the recent success of long-tailed object detection, almost all l...
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11/05/2021
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Model quantization has emerged as an indispensable technique to accelera...
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10/11/2021
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has...
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09/11/2021
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
Deep neural networks (DNNs) are vulnerable to adversarial noises, which ...
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03/22/2021
Incorporating Convolution Designs into Visual Transformers
Motivated by the success of Transformers in natural language processing ...
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03/01/2021
Diversifying Sample Generation for Accurate Data-Free Quantization
Quantization has emerged as one of the most prevalent approaches to comp...
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02/10/2021
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
We study the challenging task of neural network quantization without end...
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11/19/2020
Learning in School: Multi-teacher Knowledge Inversion for Data-Free Quantization
User data confidentiality protection is becoming a rising challenge in t...
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03/17/2020
Efficient Bitwidth Search for Practical Mixed Precision Neural Network
Network quantization has rapidly become one of the most widely used meth...
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12/29/2019
Towards Unified INT8 Training for Convolutional Neural Network
Recently low-bit (e.g., 8-bit) network quantization has been extensively...
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09/24/2019
IR-Net: Forward and Backward Information Retention for Highly Accurate Binary Neural Networks
Weight and activation binarization is an effective approach to deep neur...
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08/14/2019
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
Hardware-friendly network quantization (e.g., binary/uniform quantizatio...
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10/19/2016