Search Results for author:
Found 13 papers, 8 papers with code
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
For the issue (2), the effectiveness of ProSelfLC defends entropy minimisation.
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
Keywords: entropy minimisation, maximum entropy, confidence penalty, self knowledge distillation, label correction, label noise, semi-supervised learning, output regularisation
Instance Cross Entropy for Deep Metric Learning
Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed.
ID-aware Quality for Set-based Person Re-identification
This way it can prevent overfitting to trivial images, and alleviate the influence of outliers.
ROBUST DISCRIMINATIVE REPRESENTATION LEARNING VIA GRADIENT RESCALING: AN EMPHASIS REGULARISATION PERSPECTIVE
It is fundamental and challenging to train robust and accurate Deep Neural Networks (DNNs) when semantically abnormal examples exist.
Derivative Manipulation for General Example Weighting
By DM, we connect the design of loss function and example weighting together.
Ranked #30 on
Image Classification
on Clothing1M
(using extra training data)
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters
In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i. e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far.
Ranked #33 on
Image Classification
on Clothing1M
(using extra training data)
GAN-based Pose-aware Regulation for Video-based Person Re-identification
Video-based person re-identification deals with the inherent difficulty of matching unregulated sequences with different length and with incomplete target pose/viewpoint structure.
Ranked List Loss for Deep Metric Learning
To address this, we propose to build a set-based similarity structure by exploiting all instances in the gallery.
Deep Metric Learning by Online Soft Mining and Class-Aware Attention
Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch.
Semantic Autoencoder for Zero-Shot Learning
We show that with this additional reconstruction constraint, the learned projection function from the seen classes is able to generalise better to the new unseen classes.
Unsupervised Domain Adaptation for Zero-Shot Learning
Zero-shot learning (ZSL) can be considered as a special case of transfer learning where the source and target domains have different tasks/label spaces and the target domain is unlabelled, providing little guidance for the knowledge transfer.
