research
∙
09/12/2023
Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation
We propose a new paradigm to automatically generate training data with a...
share
research
∙
05/26/2023
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models
We focus on the challenge of out-of-distribution (OOD) detection in deep...
research
∙
05/25/2023
Batch Model Consolidation: A Multi-Task Model Consolidation Framework
In Continual Learning (CL), a model is required to learn a stream of tas...
research
∙
05/24/2023
Lightweight Learner for Shared Knowledge Lifelong Learning
In Lifelong Learning (LL), agents continually learn as they encounter ne...
research
∙
05/21/2023
Reproducibility Requires Consolidated Artifacts
Machine learning is facing a 'reproducibility crisis' where a significan...
research
∙
12/15/2022
EM-Paste: EM-guided Cut-Paste with DALL-E Augmentation for Image-level Weakly Supervised Instance Segmentation
We propose EM-PASTE: an Expectation Maximization(EM) guided Cut-Paste co...
research
∙
11/30/2022
Ferroelectric FET based Context-Switching FPGA Enabling Dynamic Reconfiguration for Adaptive Deep Learning Machines
Field Programmable Gate Array (FPGA) is widely used in acceleration of d...
research
∙
11/26/2022
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network
Transformations in the input space of Deep Neural Networks (DNN) lead to...
research
∙
07/22/2022
Neural-Sim: Learning to Generate Training Data with NeRF
Training computer vision models usually requires collecting and labeling...
research
∙
07/19/2022
Contributions of Shape, Texture, and Color in Visual Recognition
We investigate the contributions of three important features of the huma...
research
∙
06/20/2022
DALL-E for Detection: Language-driven Context Image Synthesis for Object Detection
Object cut-and-paste has become a promising approach to efficiently gene...
research
∙
06/13/2022
Invariant Structure Learning for Better Generalization and Causal Explainability
Learning the causal structure behind data is invaluable for improving ge...
research
∙
02/22/2022
Model2Detector: Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps
Out-of-distribution detection is an important capability that has long e...
research
∙
01/20/2022
What can we learn from misclassified ImageNet images?
Understanding the patterns of misclassified ImageNet images is particula...
research
∙
12/06/2021
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization
We focus on controllable disentangled representation learning (C-Dis-RL)...
research
∙
10/29/2021
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
Out-of-distribution (OOD) detection is a well-studied topic in supervise...
research
∙
09/08/2021
Video2Skill: Adapting Events in Demonstration Videos to Skills in an Environment using Cyclic MDP Homomorphisms
Humans excel at learning long-horizon tasks from demonstrations augmente...
research
∙
05/30/2021
Shaped Policy Search for Evolutionary Strategies using Waypoints
In this paper, we try to improve exploration in Blackbox methods, partic...
research
∙
05/01/2021
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
Despite substantial progress in applying neural networks (NN) to a wide ...
research
∙
11/09/2020
Lifelong Learning Without a Task Oracle
Supervised deep neural networks are known to undergo a sharp decline in ...
research
∙
10/07/2020
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Humans show an innate ability to learn the regularities of the world thr...
research
∙
10/06/2020
Unsupervised Hierarchical Concept Learning
Discovering concepts (or temporal abstractions) in an unsupervised manne...
research
∙
09/27/2020
Beneficial Perturbations Network for Defending Adversarial Examples
Adversarial training, in which a network is trained on both adversarial ...
research
∙
09/27/2020
Beneficial Perturbation Network for designing general adaptive artificial intelligence systems
The human brain is the gold standard of adaptive learning. It not only c...
research
∙
09/14/2020
Zero-shot Synthesis with Group-Supervised Learning
Visual cognition of primates is superior to that of artificial neural ne...
research
∙
06/12/2020
Attentive Feature Reuse for Multi Task Meta learning
We develop new algorithms for simultaneous learning of multiple tasks (e...
research
∙
03/19/2020
Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition
Object pose increases interclass object variance which makes object reco...
research
∙
11/23/2019
Meta Adaptation using Importance Weighted Demonstrations
Imitation learning has gained immense popularity because of its high sam...
research
∙
10/09/2019
Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system
Adversarial training, in which a network is trained on both adversarial ...
research
∙
06/25/2019
Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensory-rich environments by learning t...
research
∙
06/22/2019
Beneficial perturbation network for continual learning
Sequential learning of multiple tasks in artificial neural networks usin...
research
∙
11/03/2018
Closed-Loop GAN for continual Learning
Sequential learning of tasks using gradient descent leads to an unremitt...
research
∙
05/18/2018
Overcoming catastrophic forgetting problem by weight consolidation and long-term memory
Sequential learning of multiple tasks in artificial neural networks usin...
research
∙
05/12/2018
Born Again Neural Networks
Knowledge distillation (KD) consists of transferring knowledge from one ...
research
∙
06/07/2016
Active Long Term Memory Networks
Continual Learning in artificial neural networks suffers from interferen...
research
∙
06/06/2016
shapeDTW: shape Dynamic Time Warping
Dynamic Time Warping (DTW) is an algorithm to align temporal sequences w...
research
∙
04/28/2016
Detecting "Smart" Spammers On Social Network: A Topic Model Approach
Spammer detection on social network is a challenging problem. The rigid ...
research
∙
12/04/2015
What can we learn about CNNs from a large scale controlled object dataset?
Tolerance to image variations (e.g. translation, scale, pose, illuminati...
research
∙
07/22/2013