Papers › Towards Sustainable Self-supervised Learning
Towards Sustainable Self-supervised Learning
ShangHua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan
Although increasingly training-expensive, most self-supervised learning (SSL) models have repeatedly been trained from scratch but not fully utilized, since only a few SOTAs are employed for downstream tasks. In this work, we explore a sustainable SSL framework with two major challenges: i) learning a stronger new SSL model based on the existing pretrained SSL model, also called as "base" model, in a cost-friendly manner, ii) allowing the training of the new model to be compatible with various base models. We propose a Target-Enhanced Conditional (TEC) scheme which introduces two components to the existing mask-reconstruction based SSL. Firstly, we propose patch-relation enhanced targets which enhances the target given by base model and encourages the new model to learn semantic-relation knowledge from the base model by using incomplete inputs. This hardening and target-enhancing help the new model surpass the base model, since they enforce additional patch relation modeling to handle incomplete input. Secondly, we introduce a conditional adapter that adaptively adjusts new model prediction to align with the target of different base models. Extensive experimental results show that our TEC scheme can accelerate the learning speed, and also improve SOTA SSL base models, e.g., MAE and iBOT, taking an explorative step towards sustainable SSL.
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Code
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Code Syntology ran Syntology
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | COCO minival | TEC(VIT-B, Mask-RCNN) | box AP | 54.6 | #54 of 220 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet (finetuned) | TEC_MAE (ViT-L/16, 224) | Top 1 Accuracy | 86.5% | #13 of 65 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | TEC (Vit-B, Upernet) | Validation mIoU | 51.0 | #104 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL+FT, mmseg) | mIoU (test) | 62.5 | #1 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL+FT, mmseg) | mIoU (val) | 63.2 | #1 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL+FT) | mIoU (val) | 62.0 | #3 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL, mmseg) | mIoU (test) | 46.0 | #13 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL, mmseg) | mIoU (val) | 46.1 | #13 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL) | mIoU (val) | 42.9 | #14 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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