# Improving task-specific representation via 1M unlabelled images without any extra knowledge

Source: [Improving task-specific representation via 1M unlabelled images without any extra knowledge](<https://www.sota2.com/research/paper/68fa67c91e209131cea185f7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:22:05.587Z

Authors: Aayush Bansal

Published: 2020-06-24T17:49:05.000Z

## Abstract

We present a case-study to improve the task-specific representation by leveraging a million unlabelled images without any extra knowledge. We propose an exceedingly simple method of conditioning an existing representation on a diverse data distribution and observe that a model trained on diverse examples acts as a better initialization. We extensively study our findings for the task of surface normal estimation and semantic segmentation from a single image. We improve surface normal estimation on NYU-v2 depth dataset and semantic segmentation on PASCAL VOC by 4% over base model. We did not use any task-specific knowledge or auxiliary tasks, neither changed hyper-parameters nor made any modification in the underlying neural network architecture.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.13919>)

[PDF](<https://arxiv.org/pdf/2006.13919.pdf>)