Dhruv Mahajan | DeepAI

DeepAI

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∙ 05/27/2022

Neural Basis Models for Interpretability

Due to the widespread use of complex machine learning models in real-wor...

Filip Radenovic, et al. ∙

∙ 05/27/2022

Scalable Interpretability via Polynomials

Generalized Additive Models (GAMs) have quickly become the leading choic...

Abhimanyu Dubey, et al. ∙

∙ 03/24/2022

Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals

A visual counterfactual explanation replaces image regions in a query im...

Simon Vandenhende, et al. ∙

∙ 01/20/2022

Revisiting Weakly Supervised Pre-Training of Visual Perception Models

Model pre-training is a cornerstone of modern visual recognition systems...

Mannat Singh, et al. ∙

∙ 12/09/2021

Adaptive Methods for Aggregated Domain Generalization

Domain generalization involves learning a classifier from a heterogeneou...

Xavier Thomas, et al. ∙

∙ 05/24/2021

Large-Scale Attribute-Object Compositions

We study the problem of learning how to predict attribute-object composi...

Filip Radenovic, et al. ∙

∙ 03/29/2021

Adaptive Methods for Real-World Domain Generalization

Invariant approaches have been remarkably successful in tackling the pro...

Abhimanyu Dubey, et al. ∙

∙ 03/23/2021

Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency

Weakly supervised instance segmentation reduces the cost of annotations ...

Qing Liu, et al. ∙

∙ 08/13/2020

What leads to generalization of object proposals?

Object proposal generation is often the first step in many detection mod...

Rui Wang, et al. ∙

∙ 01/09/2020

Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias

Existing models often leverage co-occurrences between objects and their ...

Krishna Kumar Singh, et al. ∙

∙ 12/21/2019

Measuring Dataset Granularity

Despite the increasing visibility of fine-grained recognition in our fie...

Yin Cui, et al. ∙

∙ 12/20/2019

From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality

Blind or no-reference (NR) perceptual picture quality prediction is a di...

Zhenqiang Ying, et al. ∙

∙ 12/06/2019

ClusterFit: Improving Generalization of Visual Representations

Pre-training convolutional neural networks with weakly-supervised and se...

Xueting Yan, et al. ∙

∙ 11/28/2019

Self-Supervised Learning by Cross-Modal Audio-Video Clustering

The visual and audio modalities are highly correlated yet they contain d...

Humam Alwassel, et al. ∙

∙ 05/03/2019

Scaling and Benchmarking Self-Supervised Visual Representation Learning

Self-supervised learning aims to learn representations from the data its...

Priya Goyal, et al. ∙

∙ 05/02/2019

Large-scale weakly-supervised pre-training for video action recognition

Current fully-supervised video datasets consist of only a few hundred th...

Deepti Ghadiyaram, et al. ∙

∙ 05/02/2019

Billion-scale semi-supervised learning for image classification

This paper presents a study of semi-supervised learning with large convo...

I. Zeki Yalniz, et al. ∙

∙ 04/02/2019

Activity Driven Weakly Supervised Object Detection

Weakly supervised object detection aims at reducing the amount of superv...

Zhenheng Yang, et al. ∙

∙ 03/05/2019

Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search

A plethora of recent work has shown that convolutional networks are not ...

Abhimanyu Dubey, et al. ∙

∙ 05/02/2018

Exploring the Limits of Weakly Supervised Pretraining

State-of-the-art visual perception models for a wide range of tasks rely...

Dhruv Mahajan, et al. ∙

∙ 02/01/2018

Distributed Newton Methods for Deep Neural Networks

Deep learning involves a difficult non-convex optimization problem with ...

Chien-Chih Wang, et al. ∙

∙ 11/15/2017

Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles

For many applications, an ensemble of base classifiers is an effective s...

Dhruv Mahajan, et al. ∙

∙ 03/30/2016

Towards Geo-Distributed Machine Learning

Latency to end-users and regulatory requirements push large companies to...

Ignacio Cano, et al. ∙