research
∙
04/10/2023
Binary Latent Diffusion
In this paper, we show that a binary latent space can be explored for co...
share
research
∙
02/28/2023
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation
Data-free Knowledge Distillation (DFKD) has gained popularity recently, ...
research
∙
02/02/2023
Energy-Inspired Self-Supervised Pretraining for Vision Models
Motivated by the fact that forward and backward passes of a deep network...
research
∙
08/31/2022
Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition
This paper looks at semi-supervised learning (SSL) for image-based text ...
research
∙
02/01/2022
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
In this paper, we question the rationale behind propagating large number...
research
∙
11/05/2021
Exploiting a Zoo of Checkpoints for Unseen Tasks
There are so many models in the literature that it is difficult for prac...
research
∙
10/19/2021
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks
Federated learning—multi-party, distributed learning in a decentralized ...
research
∙
08/17/2021
Adaptive Convolutions with Per-pixel Dynamic Filter Atom
Applying feature dependent network weights have been proved to be effect...
research
∙
12/05/2020
Cirrus: A Long-range Bi-pattern LiDAR Dataset
In this paper, we introduce Cirrus, a new long-range bi-pattern LiDAR pu...
research
∙
11/19/2020
Using Text to Teach Image Retrieval
Image retrieval relies heavily on the quality of the data modeling and t...
research
∙
10/20/2020
Learning to Learn Variational Semantic Memory
In this paper, we introduce variational semantic memory into meta-learni...
research
∙
09/04/2020
ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution
Convolutional Neural Networks (CNNs) are known to be significantly over-...
research
∙
08/04/2020
Graph Neural Networks with Low-rank Learnable Local Filters
For the classification of graph data consisting of features sampled on a...
research
∙
07/15/2020
Learning to Learn with Variational Information Bottleneck for Domain Generalization
Domain generalization models learn to generalize to previously unseen do...
research
∙
07/13/2020
Low to High Dimensional Modality Hallucination using Aggregated Fields of View
Real-world robotics systems deal with data from a multitude of modalitie...
research
∙
04/03/2020
Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method
Active illumination is a prominent complement to enhance 2D face recogni...
research
∙
10/23/2019
SalGaze: Personalizing Gaze Estimation Using Visual Saliency
Traditional gaze estimation methods typically require explicit user cali...
research
∙
09/26/2019
Range Adaptation for 3D Object Detection in LiDAR
LiDAR-based 3D object detection plays a crucial role in modern autonomou...
research
∙
09/25/2019
Stochastic Conditional Generative Networks with Basis Decomposition
While generative adversarial networks (GANs) have revolutionized machine...
research
∙
09/25/2019
Domain-invariant Learning using Adaptive Filter Decomposition
Domain shifts are frequently encountered in real-world scenarios. In thi...
research
∙
09/24/2019
Scale-Equivariant Neural Networks with Decomposed Convolutional Filters
Encoding the input scale information explicitly into the representation ...
research
∙
09/17/2019
Single-shot 3D shape reconstruction using deep convolutional neural networks
A robust single-shot 3D shape reconstruction technique integrating the f...
research
∙
08/18/2018
In Defense of Single-column Networks for Crowd Counting
Crowd counting usually addressed by density estimation becomes an increa...
research
∙
07/04/2018
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
Lane detection is to detect lanes on the road and provide the accurate l...
research
∙
05/18/2018
Learning to Collaborate for User-Controlled Privacy
It is becoming increasingly clear that users should own and control thei...
research
∙
05/18/2018
Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning
Deep neural networks (DNNs) typically have enough capacity to fit random...
research
∙
05/17/2018
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
Explicit encoding of group actions in deep features makes it possible fo...
research
∙
04/03/2018
Weakly Supervised Instance Segmentation using Class Peak Response
Weakly supervised instance segmentation with image-level labels, instead...
research
∙
03/15/2018
Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification
In this paper we introduce an ensemble method for convolutional neural n...
research
∙
02/12/2018
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Filters in a Convolutional Neural Network (CNN) contain model parameters...
research
∙
12/05/2017
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
Deep neural networks trained using a softmax layer at the top and the cr...
research
∙
11/22/2017
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Hash codes are efficient data representations for coping with the ever g...
research
∙
11/16/2017
LDMNet: Low Dimensional Manifold Regularized Neural Networks
Deep neural networks have proved very successful on archetypal tasks for...
research
∙
09/06/2017
Soft Proposal Networks for Weakly Supervised Object Localization
Weakly supervised object localization remains challenging, where only im...
research
∙
05/23/2017
Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems
Security, privacy, and fairness have become critical in the era of data ...
research
∙
01/07/2017
Oriented Response Networks
Deep Convolution Neural Networks (DCNNs) are capable of learning unprece...
research
∙
11/22/2016
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model
In this paper, a self-learning approach is proposed towards solving scen...
research
∙
11/21/2016
Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral Hallucination and Low-rank Embedding
Surveillance cameras today often capture NIR (near infrared) images in l...
research
∙
12/21/2015
GraphConnect: A Regularization Framework for Neural Networks
Deep neural networks have proved very successful in domains where large ...
research
∙
12/18/2014
Data Representation using the Weyl Transform
The Weyl transform is introduced as a rich framework for data representa...
research
∙
12/16/2014
Random Forests Can Hash
Hash codes are a very efficient data representation needed to be able to...
research
∙
12/19/2013
Learning Transformations for Classification Forests
This work introduces a transformation-based learner model for classifica...
research
∙
09/09/2013
Learning Transformations for Clustering and Classification
A low-rank transformation learning framework for subspace clustering and...
research
∙
08/01/2013
Sparse Dictionary-based Attributes for Action Recognition and Summarization
We present an approach for dictionary learning of action attributes via ...
research
∙
08/01/2013
Domain-invariant Face Recognition using Learned Low-rank Transformation
We present a low-rank transformation approach to compensate for face var...
research
∙
08/01/2013
Learning Robust Subspace Clustering
We propose a low-rank transformation-learning framework to robustify sub...
research
∙
08/01/2013
Compositional Dictionaries for Domain Adaptive Face Recognition
We present a dictionary learning approach to compensate for the transfor...
research
∙
08/21/2012
A Unified Approach for Modeling and Recognition of Individual Actions and Group Activities
Recognizing group activities is challenging due to the difficulties in i...
research
∙
08/17/2012