π
π
Old Age
What Do Deep CNNs Learn About Objects?
April 09, 2015 Β· Declared Dead Β· π International Conference on Learning Representations
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko
arXiv ID
1504.02485
Category
cs.CV: Computer Vision
Citations
3
Venue
International Conference on Learning Representations
Last Checked
5 months ago
Abstract
Deep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters. What exactly do these parameters represent? Recent work has started to analyse CNN representations, finding that, e.g., they are invariant to some 2D transformations Fischer et al. (2014), but are confused by particular types of image noise Nguyen et al. (2014). In this work, we delve deeper and ask: how invariant are CNNs to object-class variations caused by 3D shape, pose, and photorealism?
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Computer Vision
π
π
Old Age
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
π
π
Old Age
SSD: Single Shot MultiBox Detector
π
π
Old Age
Squeeze-and-Excitation Networks
π
π
Old Age
Fast R-CNN
π
π
Old Age
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted