# Evolving Losses for Unlabeled Video Representation Learning

arXiv: https://arxiv.org/abs/1906.03248 · HTML: https://mlsanity.com/paper/1906.03248/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?](https://mlsanity.com/paper/2206.05266/) (2022)
- [Video Representation Learning with Joint-Embedding Predictive Architectures](https://mlsanity.com/paper/2412.10925/) (2024)

## Abstract (arXiv)

We present a new method to learn video representations from unlabeled data. Given large-scale unlabeled video data, the objective is to benefit from such data by learning a generic and transferable representation space that can be directly used for a new task such as zero/few-shot learning. We formulate our unsupervised representation learning as a multi-modal, multi-task learning problem, where the representations are also shared across different modalities via distillation. Further, we also introduce the concept of finding a better loss function to train such multi-task multi-modal representation space using an evolutionary algorithm; our method automatically searches over different combinations of loss functions capturing multiple (self-supervised) tasks and modalities. Our formulation allows for the distillation of audio, optical flow and temporal information into a single, RGB-based convolutional neural network. We also compare the effects of using additional unlabeled video data and evaluate our representation learning on standard public video datasets.