Search Results for author:
Found 20 papers, 8 papers with code
Reducing Distributional Uncertainty by Mutual Information Maximisation and Transferable Feature Learning
Distributional uncertainty exists broadly in many real-world applications, one of which in the form of domain discrepancy.
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
For the issue (2), the effectiveness of ProSelfLC defends entropy minimisation.
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge
Additionally, CMD covers two special cases: zero-knowledge and all knowledge, leading to a unified MKD framework.
Deep Learning Proteins using a Triplet-BERT network
Modern sequencing technology has produced a vast quantity of proteomic data, which has been key to the development of various deep learning models within the field.
Modelling Drug-Target Binding Affinity using a BERT based Graph Neural network
In this paper, we seek to leverage a set of BERT-style models that have been pre-trained on vast quantities of both protein and drug data.
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition
This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results.
An Image Analogies Approach for Multi-Scale Contour Detection
Given such a reference image, we present a new method based on this expertise to locate contours of a query image in the same way that it is done for the reference (i. e by analogy).
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation
We tackle this problem by proposing an off-policy RL learning algorithm where a behaviour policy represented by GRUs performs the sampling.
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
DADA: Differentiable Automatic Data Augmentation
In this paper, we propose Differentiable Automatic Data Augmentation (DADA) which dramatically reduces the cost.
Ranked #15 on
Data Augmentation
on ImageNet
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.
Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detection
Despite this, we notice that the semantic ambiguity greatly degrades the detection performance.
Ranked #1 on
Face Alignment
on 300W
(NME_inter-pupil (%, Full) metric)
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.
Attribute-Enhanced Face Recognition With Neural Tensor Fusion Networks
To solve this problem, we establish a theoretical equivalence between tensor optimisation and a two-stream gated neural network.
Deep Convolutional Poses for Human Interaction Recognition in Monocular Videos
With the development of deep models for the human pose estimation problem, this work aims to verify the effectiveness of using the human pose in order to recognize the human interaction in monocular videos.
