Research

Papers for “imagenet”

Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.

Title	Authors	Year	Source
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints	Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp	2022	arxiv 2212.05055
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks	Dang Nguyen, Trang Nguyen, Khai Nguyen	2021	arxiv 2110.15538
Corrupted Image Modeling for Self-Supervised Visual Pre-Training	Yuxin Fang, Li Dong, Hangbo Bao	2022	arxiv 2202.03382
Practical Network Acceleration with Tiny Sets	Guo-Hua Wang, Jianxin Wu	2022	arxiv 2202.07861
Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning	Yi Lin, Zhongchen Zhao, Zhengjie ZHU	2023	arxiv 2303.13122
Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound	Blake VanBerlo, Brian Li, Alexander Wong	2023	arxiv 2304.02724
GMConv: Modulating Effective Receptive Fields for Convolutional Kernels	Qi Chen, Chao Li, Jia Ning	2023	arxiv 2302.04544
CrossSplit: Mitigating Label Noise Memorization through Data Splitting	Jihye Kim, Aristide Baratin, Yan Zhang	2022	arxiv 2212.01674
Picking Up Quantization Steps for Compressed Image Classification	Li Ma, Peixi Peng, Guangyao Chen	2023	arxiv 2304.10714
Mimetic Initialization of Self-Attention Layers	Asher Trockman, J. Zico Kolter	2023	arxiv 2305.09828
PFGE: Parsimonious Fast Geometric Ensembling of DNNs	Hao Guo, Jiyong Jin, Bin Liu	2022	arxiv 2202.06658
Robust Perception through Equivariance	Chengzhi Mao, Lingyu Zhang, Abhishek Joshi	2022	arxiv 2212.06079
Magnitude Attention-based Dynamic Pruning	Jihye Back, Namhyuk Ahn, Jangho Kim	2023	arxiv 2306.05056
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks	Suo Qiu, Xiangmin Xu, Bolun Cai	2017	arxiv 1706.08098
Combining Multiple Cues for Visual Madlibs Question Answering	Tatiana Tommasi, Arun Mallya, Bryan Plummer	2016	arxiv 1611.00393
Don't Decay the Learning Rate, Increase the Batch Size	Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying	2017	arxiv 1711.00489
Deep Co-Training for Semi-Supervised Image Recognition	Siyuan Qiao, Wei Shen, Zhishuai Zhang	2018	arxiv 1803.05984
Study of Residual Networks for Image Recognition	Mohammad Sadegh Ebrahimi, Hossein Karkeh Abadi	2018	arxiv 1805.00325
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning	Arun Mallya, Svetlana Lazebnik	2017	arxiv 1711.05769
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects	Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber	2018	arxiv 1804.00525

Search results