Search Results for author:
Found 9 papers, 2 papers with code
Learning with Diversity: Self-Expanded Equalization for Better Generalized Deep Metric Learning
Most existing DML methods focus on improving the model robustness against category shift to keep the performance on unseen categories.
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
With the training going on, the model begins to overfit noisy pairs.
Mutual Quantization for Cross-Modal Search With Noisy Labels
More specifically, we propose a proxy-based contrastive (PC) loss to mitigate the gap between different modalities and train networks for different modalities jointly with small-loss samples that are selected with the PC loss and a mutual quantization loss.
Understanding and Improving Early Stopping for Learning with Noisy Labels
Instead of the early stopping, which trains a whole DNN all at once, we initially train former DNN layers by optimizing the DNN with a relatively large number of epochs.
Ranked #8 on
Learning with noisy labels
on CIFAR-10N-Aggregate
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
Motivated by that classifiers mostly output Bayes optimal labels for prediction, in this paper, we study to directly model the transition from Bayes optimal labels to noisy labels (i. e., Bayes-label transition matrix (BLTM)) and learn a classifier to predict Bayes optimal labels.
Learning With Privileged Tasks
Concretely, we introduce a privileged parameter so that the optimization direction does not necessarily follow the gradient from the privileged tasks, but concentrates more on the target tasks.
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
To address this issue, we propose a novel deep unsupervised hashing model, dubbed DistillHash, which can learn a distilled data set consisted of data pairs, which have confidence similarity signals.
Shared Predictive Cross-Modal Deep Quantization
In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search.
Semantic Structure-based Unsupervised Deep Hashing
Hashing is becoming increasingly popular for approximate nearest neighbor searching in massive databases due to its storage and search efficiency.
