A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation

16 March 2023

Papers citing "A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation"

8 / 8 papers shown

Title
Towards Domain Adaptive Neural Contextual Bandits Ziyan Wang Hao Wang Hao Wang 50 0 0 13 Jun 2024
Enhancing Domain Adaptation through Prompt Gradient Alignment Hoang Phan Lam C. Tran Quyen Tran Trung Le 52 0 0 13 Jun 2024
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning Jiaxi Lv Yi Huang Mingfu Yan Jiancheng Huang Jianzhuang Liu Yifan Liu Yafei Wen Xiaoxin Chen Shifeng Chen VGen DiffM 30 23 0 21 Nov 2023
Rethinking Supervised Pre-training for Better Downstream Transferring Yutong Feng Jianwen Jiang Mingqian Tang R. L. Jin Yue Gao SSL 43 39 0 12 Oct 2021
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 271 2,603 0 04 May 2021
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 176 686 0 22 Apr 2021
Domain-Adversarial Training of Neural Networks Yaroslav Ganin E. Ustinova Hana Ajakan Pascal Germain Hugo Larochelle François Laviolette M. Marchand Victor Lempitsky GAN OOD 177 9,327 0 28 May 2015
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,194 0 01 Sep 2014