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Found 12 papers, 9 papers with code
AdaTriplet-RA: Domain Matching via Adaptive Triplet and Reinforced Attention for Unsupervised Domain Adaptation
Unsupervised domain adaption (UDA) is a transfer learning task where the data and annotations of the source domain are available but only have access to the unlabeled target data during training.
Misspecified Phase Retrieval with Generative Priors
In this paper, we study phase retrieval under model misspecification and generative priors.
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.
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)
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.
