research
∙
08/24/2023
Label Budget Allocation in Multi-Task Learning
The cost of labeling data often limits the performance of machine learni...
share
research
∙
06/01/2023
StyleDrop: Text-to-Image Generation in Any Style
Pre-trained large text-to-image models synthesize impressive images with...
research
∙
06/01/2023
Learning Disentangled Prompts for Compositional Image Synthesis
We study domain-adaptive image synthesis, the problem of teaching pretra...
research
∙
02/15/2023
Video Probabilistic Diffusion Models in Projected Latent Space
Despite the remarkable progress in deep generative models, synthesizing ...
research
∙
02/10/2023
MaskSketch: Unpaired Structure-guided Masked Image Generation
Recent conditional image generation methods produce images of remarkable...
research
∙
02/06/2023
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
In Composed Image Retrieval (CIR), a user combines a query image with te...
research
∙
11/30/2022
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Semi-supervised anomaly detection is a common problem, as often the data...
research
∙
10/03/2022
Visual Prompt Tuning for Generative Transfer Learning
Transferring knowledge from an image synthesis model trained on a large ...
research
∙
06/02/2022
Prefix Conditioning Unifies Language and Label Supervision
Vision-language contrastive learning suggests a new learning paradigm by...
research
∙
05/27/2022
Federated Semi-Supervised Learning with Prototypical Networks
With the increasing computing power of edge devices, Federated Learning ...
research
∙
01/10/2022
Towards Group Robustness in the presence of Partial Group Labels
Learning invariant representations is an important requirement when trai...
research
∙
12/21/2021
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
We introduce anomaly clustering, whose goal is to group data into semant...
research
∙
07/30/2021
Object-aware Contrastive Learning for Debiased Scene Representation
Contrastive self-supervised learning has shown impressive results in lea...
research
∙
06/14/2021
Controlling Neural Networks with Rule Representations
We propose a novel training method to integrate rules into deep learning...
research
∙
06/11/2021
Self-Trained One-class Classification for Unsupervised Anomaly Detection
Anomaly detection (AD), separating anomalies from normal data, has vario...
research
∙
06/08/2021
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
We extend semi-supervised learning to the problem of domain adaptation t...
research
∙
04/08/2021
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
We aim at constructing a high performance model for defect detection tha...
research
∙
02/18/2021
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning
Semi-supervised learning on class-imbalanced data, although a realistic ...
research
∙
11/04/2020
Learning and Evaluating Representations for Deep One-class Classification
We present a two-stage framework for deep one-class classification. We f...
research
∙
10/17/2020
i-Mix: A Strategy for Regularizing Contrastive Representation Learning
Contrastive representation learning has shown to be an effective way of ...
research
∙
07/14/2020
Improving Face Recognition by Clustering Unlabeled Faces in the Wild
While deep face recognition has benefited significantly from large-scale...
research
∙
05/10/2020
A Simple Semi-Supervised Learning Framework for Object Detection
Semi-supervised learning (SSL) has promising potential for improving the...
research
∙
02/26/2020
Towards Universal Representation Learning for Deep Face Recognition
Recognizing wild faces is extremely hard as they appear with all kinds o...
research
∙
01/21/2020
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Semi-supervised learning (SSL) provides an effective means of leveraging...
research
∙
11/22/2019
Adversarial Learning of Privacy-Preserving and Task-Oriented Representations
Data privacy has emerged as an important issue as data-driven deep learn...
research
∙
11/21/2019
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
We improve the recently-proposed "MixMatch" semi-supervised learning alg...
research
∙
06/05/2019
Adaptation Across Extreme Variations using Unlabeled Domain Bridges
We tackle an unsupervised domain adaptation problem for which the domain...
research
∙
04/16/2019
Active Adversarial Domain Adaptation
We propose an active learning approach for transferring representations ...
research
∙
01/16/2019
Domain Adaptation for Structured Output via Discriminative Representations
Predicting structured outputs such as semantic segmentation relies on ex...
research
∙
01/16/2019
Domain Adaptation for Structured Output via Discriminative Patch Representations
Predicting structured outputs such as semantic segmentation relies on ex...
research
∙
03/23/2018
Feature Transfer Learning for Deep Face Recognition with Long-Tail Data
Real-world face recognition datasets exhibit long-tail characteristics, ...
research
∙
02/28/2018
Joint Pixel and Feature-level Domain Adaptation in the Wild
Recent developments in deep domain adaptation have allowed knowledge tra...
research
∙
02/28/2018
Learning to Adapt Structured Output Space for Semantic Segmentation
Convolutional neural network-based approaches for semantic segmentation ...
research
∙
08/07/2017
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Despite rapid advances in face recognition, there remains a clear gap be...
research
∙
06/12/2017
Channel-Recurrent Variational Autoencoders
Variational Autoencoder (VAE) is an efficient framework in modeling natu...
research
∙
02/10/2017
Reconstruction-Based Disentanglement for Pose-invariant Face Recognition
Deep neural networks (DNNs) trained on large-scale datasets have recentl...
research
∙
03/16/2016
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Recently, convolutional neural networks (CNNs) have been used as a power...
research
∙
12/02/2015
Attribute2Image: Conditional Image Generation from Visual Attributes
This paper investigates a novel problem of generating images from visual...
research
∙
04/13/2015
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Object detection systems based on the deep convolutional neural network ...
research
∙
06/27/2012