Gaussian, Motion & Defocus Blur Classification
In this story, Blind Image Blur Estimation via Deep Learning, by Nanjing University of Information Science and Technology, The University of Sheffield, and Northumbria University, is reviewed. In this paper:
- A learning-based method using a pre-trained deep neural network (DNN) and a general regression neural network (GRNN) is proposed to first classify the blur type and then estimate its parameters.
This is a paper in 2016 TIP with over 100 citations where TIP has high impact factor of 10.856. (Sik-Ho Tsang @ Medium)
Outline
- DNN & GRNN Framework
- Deep Neural Network (DNN)
- General Regression Neural Network (GRNN)
- Experimental Results
1. DNN & GRNN Framework
1.1. Framework
- DNN is…
Mean Teacher, Teacher Student Approach, for Semi-Supervised Learning
In this story, Mean Teachers are Better Role Models: Weight-Averaged Consistency Targets Improve Semi-Supervised Deep Learning Results, by The Curious AI Company, and Aalto University, is reviewed. In this paper:
- Mean Teacher is proposed, to average the model weights instead of label predictions as in Temporal Ensembling [13].
- Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling [13].
- Without changing the network architecture, Mean Teacher achieves a lower error rate.
This is a paper in 2017 NeurIPS with over 1300 citations. (Sik-Ho Tsang @ Medium) Teacher-Student approach can have other usages such as knowledge distillation…
DeepCluster, K-Mean Clustering to Generate Pseudo-Labels, a Pretext Task for Self-Supervised Learning
In this story, Deep Clustering for Unsupervised Learning of Visual Features, DeepCluster, by Facebook AI Research, is reviewed. In this paper:
- DeepCluster, a clustering method is proposed that jointly learns the parameters of a neural network and the cluster assignments of the resulting features.
- DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network.
This is a paper in 2018 ECCV with over 900 citations. (Sik-Ho Tsang @ Medium)
Outline
- Notations for Supervised Learning
- DeepCluster as Pretext Task in Self-Supervised Learning
- DeepCluster Analysis
- DeepCluster Performance
1. Notations for Supervised Learning
- Before…
SepConv++: A Bunch of Small Improvements for Adaptive Separable Convolutions, Achieve SOTA Performance
In this story, Revisiting Adaptive Convolutions for Video Frame Interpolation, by Adobe Research, is reviewed. In this paper:
- Network using adaptive separable convolutions, is improved by a subtle set of low level improvements.
- These improvements are delayed padding (+0.37 dB), input normalization (+0.30 dB), network improvements (+0.42 dB), kernel normalization (+0.52 dB), contextual training (+0.18 dB), self-ensembling (+0.18 dB).
With all these improvements, it is a paper published in 2021 WACV. (Sik-Ho Tsang @ Medium)
Outline
- Proposed Video Frame Interpolation Framework
- Delayed Padding (+0.37 dB)
- Input Normalization (+0.30 dB)
- Network Improvements (+0.42 dB)
- Kernel Normalization (+0.52 dB)
- Contextual Training (+0.18 dB)
- …
FCOS: Training Without the Use of Anchor Boxes
In this paper, FCOS: Fully Convolutional One-Stage Object Detection, by The University of Adelaide, is reviewed. In this paper:
FCOS is designed in which it is anchor box free, as well as proposal free.
- FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.
- It also avoids all hyper-parameters related to anchor boxes.
- FCOS encourages to rethink the need of anchor boxes.
This is a paper in 2019 ICCV with over 1000 citations. (Sik-Ho Tsang @ Medium)
Outline
- FCOS: Network Architecture
- Multi-level Prediction with FPN for FCOS
- Ablation Study
- SOTA Comparison
1. FCOS: Network Architecture
RotNet: Self-Supervised Learning by Predicting Image Rotations
In this story, Unsupervised Representation Learning by Predicting Image Rotations, by University Paris-Est, is reviewed. In this paper:
- Using RotNet, image features are learnt by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input.
- By this mean, unsupervised pre-trained AlexNet model achieves the state-of-the-art mAP of 54.4% that is only 2.4 points lower from the supervised AlexNet.
This is a paper in 2018 ICLR with over 1100 citations. (Sik-Ho Tsang @ Medium)
Outline
- RotNet: Image Rotation Prediction Framework
- Ablation Study & SOTA Comparison on CIFAR-10
- Task Generalization on ImageNet, Places, & PASCAL…
Solving Jigsaw Puzzles as Pretext Task for Self-Supervised Learning
In this paper, Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles, Jigsaw Puzzles / CFN, by University of Bern, is reviewed. In this paper:
- Solving Jigsaw puzzles is treated as a pretext task, which requires no manual labeling. By training the CFN to solve Jigsaw puzzles, both a feature mapping of object parts and their correct spatial arrangement, are learnt.
- Specifically, the Context Free Network (CFN), a siamese-ennead CNN, is designed to take image tiles as input, and outputs the correct spatial arrangement. …
Colorization as Pretext Task in Self-Supervised Learning, Outperforms Context Prediction & Context Encoders
In this story, Colorful Image Colorization, by University of California, Berkeley, is reviewed. In this paper:
- A fully automatic approach is designed, which produces vibrant and realistic colorizations for a grayscale image.
- This proposed colorization task is treated as a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder.
It is a paper in 2016 ECCV with over 1900 citations. (Sik-Ho Tsang @ Medium)
Outline
- Colorful Image Colorization
- Colorization Results
- Self-Supervised Learning Results
1. Colorful Image Colorization
Besides Using Latent Vector z, Latent Code c is also Input to GAN, for Learning Disentangled Representations
In this story, Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, (InfoGAN), by OpenAI, is reviewed. In this paper:
- InfoGAN is designed to maximize the mutual information between a small subset of the latent variables and the observation.
- A lower bound of the mutual information objective is derived that can be optimized efficiently.
- By doing so, InfoGAN successfully disentangles writing styles from digit shapes on MNIST dataset, and disentangles the visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset.
Split-Brain Auto for Self-Supervised Learning, Outperforms Jigsaw Puzzles, Context Prediction, ALI/BiGAN, L³-Net, Context Encoders, etc.
In this paper, Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction, (Split-Brain Auto), by Berkeley AI Research (BAIR) Laboratory, University of California, is reviewed. In this paper:
- A network is split into two sub-networks, each is trained to perform a difficult task — predicting one subset of the data channels from another.
- By forcing the network to solve cross-channel prediction tasks, feature learning is achieved without using any labels.
This is a paper in 2017 CVPR with over 400 citations. (Sik-Ho Tsang @ Medium)
Outline
- Split-Brain Autoencoder (Split-Brain Auto)
- Experimental Results
1. Split-Brain Autoencoders (Split-Brain Auto)
Sik-Ho Tsang
PhD, Researcher. I share what I've learnt and done. :) My LinkedIn: https://www.linkedin.com/in/sh-tsang/, My Paper Reading List: https://bit.ly/33TDhxG
