Search Results for author:
Found 11 papers, 7 papers with code
FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation
In this paper, motivated by object-oriented programming, we model the subject as a derived class whose base class is its semantic category.
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
To solve this problem, we propose \textbf{InfoBatch}, a novel framework aiming to achieve lossless training acceleration by unbiased dynamic data pruning.
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All in One Classifier
The SCL loss weakens the adverse effects of the data augmentation view-noise problem which is amplified in domain transfer tasks.
Ranked #3 on
Universal Domain Adaptation
on Office-31
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling
Intuitively, FR algorithms can benefit from both the estimation of uncertainty and the detection of out-of-distribution (OOD) samples.
EVNet: An Explainable Deep Network for Dimension Reduction
The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability.
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier
The SCL loss weakens the adverse effects of the data augmentation view-noise problem which is amplified in domain transfer tasks.
DLME: Deep Local-flatness Manifold Embedding
To overcome the underconstrained embedding problem, we design a loss and theoretically demonstrate that it leads to a more suitable embedding based on the local flatness.
Ranked #2 on
Image Classification
on ImageNet-100
Unsupervised Domain Adaptation By Optimal Transportation Of Clusters Between Domains
Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain.
Dash: Semi-Supervised Learning with Dynamic Thresholding
In this work we develop a simple yet powerful framework, whose key idea is to select a subset of training examples from the unlabeled data when performing existing SSL methods so that only the unlabeled examples with pseudo labels related to the labeled data will be used to train models.
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
The set of triplet constraints has to be sampled within the mini-batch.
Ranked #21 on
Metric Learning
on CUB-200-2011
(using extra training data)
