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Found 7 papers, 2 papers with code
Learning Low-Rank Representations for Model Compression
Vector Quantization (VQ) is an appealing model compression method to obtain a tiny model with less accuracy loss.
Learning specialized activation functions with the Piecewise Linear Unit
It can learn specialized activation functions and achieves SOTA performance on large-scale datasets like ImageNet and COCO.
FixNorm: Dissecting Weight Decay for Training Deep Neural Networks
Based on this discovery, we propose a new training method called FixNorm, which discards weight decay and directly controls the two mechanisms.
Diving into Optimization of Topology in Neural Networks
Seeking effective networks has become one of the most crucial and practical areas in deep learning.
Towards Flops-constrained Face Recognition
Large scale face recognition is challenging especially when the computational budget is limited.
Fully Motion-Aware Network for Video Object Detection
Video objection detection is challenging in the presence of appearance deterioration in certain video frames.
Crafting GBD-Net for Object Detection
The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
