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D. Mahajan

32 papers hereh-index 347k citations82 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author7
  • middle author14
  • last author10

Across the 31 of 32 papers where every author was matched, so the position is known.

fields
  • cs.CV20
  • cs.LG10
  • q-fin.TR1
  • stat.ML1
same name
  • D. Mahajan — 1 paper, h 2
  • D. Mahajan — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132023
most citedBillion-scale semi-supervised learning for image classification

330 citations · 765 across the 27 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021

Adaptive Methods for Aggregated Domain Generalization

Xavier Thomas, Dhruv Mahajan, Alex Pentland +1

Domain generalization involves learning a classifier from a heterogeneous collection of training sources such that it generalizes to data drawn from similar unknown target domains,…

cs.CV2021★ 4 cited

Large-Scale Attribute-Object Compositions

Filip Radenovic, Animesh Sinha, Albert Gordo +2

We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data. To the best…

cs.CV2021

Adaptive Methods for Real-World Domain Generalization

Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland +1

Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different f…

cs.CV2021★ 1 cited

Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency

Qing Liu, Vignesh Ramanathan, Dhruv Mahajan +2

Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predomina…

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