# ODC¶

## Abstract¶

Joint clustering and feature learning methods have shown remarkable performance in unsupervised representation learning. However, the training schedule alternating between feature clustering and network parameters update leads to unstable learning of visual representations. To overcome this challenge, we propose Online Deep Clustering (ODC) that performs clustering and network update simultaneously rather than alternatingly. Our key insight is that the cluster centroids should evolve steadily in keeping the classifier stably updated. Specifically, we design and maintain two dynamic memory modules, i.e., samples memory to store samples’ labels and features, and centroids memory for centroids evolution. We break down the abrupt global clustering into steady memory update and batch-wise label re-assignment. The process is integrated into network update iterations. In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly. Extensive experiments demonstrate that ODC stabilizes the training process and boosts the performance effectively.

## Results and 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
resnet50_8xb64-steplr-440e feature5 78.42 32.42 40.27 49.95 59.96 65.71 69.99 73.64 75.13

#### 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
resnet50_8xb64-steplr-440e 14.76 31.82 42.44 55.76 57.70 53.42

#### 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
resnet50_8xb64-steplr-440e 19.28 34.09 40.90 47.04 48.35

#### ImageNet Nearest-Neighbor Classification¶

The results are obtained from the features after GlobalAveragePooling. Here, k=10 to 200 indicates different number of nearest neighbors.

Self-Supervised Config k=10 k=20 k=100 k=200
resnet50_8xb64-steplr-440e 38.5 39.1 37.8 36.9

## Citation¶

@inproceedings{zhan2020online,
title={Online deep clustering for unsupervised representation learning},
author={Zhan, Xiaohang and Xie, Jiahao and Liu, Ziwei and Ong, Yew-Soon and Loy, Chen Change},
booktitle={CVPR},
year={2020}
}