Table of Contents
Shortcuts

# DeepCluster

> Deep Clustering for Unsupervised Learning of Visual Features

## Abstract

Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large scale datasets. In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network.

## Results and Models

Back to model_zoo.md to download models.

In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models are pre-trained on ImageNet-1k dataset.

### Classification

The classification benchmarks includes 4 downstream task datasets, VOC, ImageNet, iNaturalist2018 and Places205. If not specified, the results are Top-1 (%).

#### VOC SVM / Low-shot SVM

The Best Layer indicates that the best results are obtained from which layers feature map. For example, if the Best Layer is feature3, its best result is obtained from the second stage of ResNet (1 for stem layer, 2-5 for 4 stage layers).

Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.

Self-Supervised Config | Best Layer | SVM | k=1 | k=2 | k=4 | k=8 | k=16 | k=32 | k=64 | k=96
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sobel_resnet50_8xb64-steplr-200e | feature5 | 74.26 | 29.37 | 37.99 | 45.85 | 55.57 | 62.48 | 66.15 | 70.00 | 71.37

#### ImageNet Linear Evaluation

The Feature1 - Feature5 don’t have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to resnet50_mhead_linear-8xb32-steplr-90e_in1k for details of config.

The AvgPool result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to resnet50_linear-8xb32-steplr-100e_in1k for details of config.

Self-Supervised Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool
--- | --- | --- | --- | --- | --- | ---
sobel_resnet50_8xb64-steplr-200e | 12.78 | 30.81 | 43.88 | 57.71 | 51.68 | 46.92

#### Places205 Linear Evaluation

The Feature1 - Feature5 don’t have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to resnet50_mhead_8xb32-steplr-28e_places205.py for details of config.

Self-Supervised Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5
--- | --- | --- | --- | --- | ---
sobel_resnet50_8xb64-steplr-200e | 18.80 | 33.93 | 41.44 | 47.22 | 42.61

## Citation

    @inproceedings{caron2018deep,
      title={Deep clustering for unsupervised learning of visual features},
      author={Caron, Mathilde and Bojanowski, Piotr and Joulin, Armand and Douze, Matthijs},
      booktitle={ECCV},
      year={2018}
    }