SOTA2 Research · papers

Papers for “imagenet”

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

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20 indexed

Title	Authors	Year	Source
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks	Abhisek Kundu, Naveen K. Mellempudi, Dharma Teja Vooturi	2023	arxiv 2304.06941
Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement	Hao Zhu, Piotr Koniusz	2023	arxiv 2304.11598
FFCV: Accelerating Training by Removing Data Bottlenecks	Guillaume Leclerc, Andrew Ilyas, Logan Engstrom	2023	arxiv 2306.12517
Denoising Diffusion Autoencoders are Unified Self-supervised Learners	Weilai Xiang, Hongyu Yang, Di Huang	2023	arxiv 2303.09769
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration	Hyekang Park, Jongyoun Noh, Youngmin Oh	2023	arxiv 2308.11911
Semantically-Prompted Language Models Improve Visual Descriptions	Michael Ogezi, Bradley Hauer, Grzegorz Kondrak	2023	arxiv 2306.06077
Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learning	Mohammad Rostami, Atik Faysal, Huaxia Wang	2024	arxiv 2402.18599
CompRess: Self-Supervised Learning by Compressing Representations	Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash	2020	arxiv 2010.14713
Data Agnostic Filter Gating for Efficient Deep Networks	Xiu Su, Shan You, Tao Huang	2020	arxiv 2010.15041
BasisNet: Two-stage Model Synthesis for Efficient Inference	Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov	2021	arxiv 2105.03014