Search Results for author:
Found 7 papers, 2 papers with code
Theoretically Principled Federated Learning for Balancing Privacy and Utility
We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility.
Contrastive Masked Autoencoders for Self-Supervised Video Hashing
To capture video semantic information for better hashing learning, we adopt an encoder-decoder structure to reconstruct the video from its temporal-masked frames.
PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework
Generative model based image lossless compression algorithms have seen a great success in improving compression ratio.
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations
In this work, we propose an effective weakly-supervised video semantic segmentation pipeline with click annotations, called WeClick, for saving laborious annotating effort by segmenting an instance of the semantic class with only a single click.
Energy Aligning for Biased Models
Training on class-imbalanced data usually results in biased models that tend to predict samples into the majority classes, which is a common and notorious problem.
Towards a category-extended object detector with limited data
Object detectors are typically learned on fully-annotated training data with fixed predefined categories.
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while other information such as model parameters or the training datasets are unknown.
