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Deep Isometric Learning for Visual Recognition
Haozhi Qi · Chong You · Xiaolong Wang · Yi Ma · Jitendra Malik

Tue Jul 14 10:00 AM -- 10:45 AM & Tue Jul 14 11:00 PM -- 11:45 PM (PDT) @

Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-the-art performance. This paper shows that deep vanilla ConvNets without normalization nor skip connections can also be trained to achieve surprisingly good performance on standard image recognition benchmarks. This is achieved by enforcing the convolution kernels to be near isometric during initialization and training, as well as by using a variant of ReLU that is shifted towards being isometric. Further experiments show that if combined with skip connections, such near isometric networks can achieve performances on par with (for ImageNet) and better than (for COCO) the standard ResNet, even without normalization at all. Our code is available at https://github.com/HaozhiQi/ISONet.

Author Information

Haozhi Qi (UC Berkeley)
Chong You (University of California, Berkeley)
Xiaolong Wang (UCSD)
Xiaolong Wang

Our group has a broad interest around the directions of Computer Vision, Machine Learning and Robotics. Our focus is on learning 3D and dynamics representations through videos and physical robotic interaction data. We explore various means of supervision signals from the data itself, language, and common sense knowledge. We leverage these comprehensive representations to facilitate the learning of robot skills, with the goal of generalizing the robot to interact effectively with a wide range of objects and environments in the real physical world. Please check out our individual research topic of Self-Supervised Learning, Video Understanding, Common Sense Reasoning, RL and Robotics, 3D Interaction, Dexterous Hand.

Yi Ma (UC Berkeley)
Jitendra Malik (University of California at Berkeley)

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