Research map / Continual learning / Class-incremental
Streaming Classification with Emerging New Class by Class Matrix Sketching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add class matrix sketching (vs original space with KNN) (ablation) | SENC-MaS · KDD Cup 99 | accuracy: 0.792 -> 0.897 | Helped |
| add class matrix sketching (vs original space with KNN) (ablation) | SENC-MaS · Forest Cover | accuracy: 0.633 -> 0.792 | Helped |
| add class matrix sketching (vs original space with KNN) (ablation) | SENC-MaS · MNIST | accuracy: 0.701 -> 0.817 | Helped |
| add class matrix sketching (vs original space with KNN) (ablation) | SENC-MaS · MNIST | F-measure: 0.652 -> 0.710 | Helped |
| SENC-MaS vs best ensemble baseline ECSMiner | SENC-MaS · Forest Cover | accuracy: 0.823 -> 0.792 | Hurt |
| SENC-MaS vs SAND-F | SENC-MaS · KDD Cup 99 | accuracy: 0.880 -> 0.897 | Helped |
| use sketching size greater than 50 percent of original data (ablation) | SENC-MaS · MNIST | accuracy | Helped |
Closest papers by meaning
- Semi-Supervised Streaming Learning with Emerging New Labels (AAAI 2020 · similarity 0.87)
- Fast Video Classification via Adaptive Cascading of Deep Models (CVPR 2017 · similarity 0.83)
- Class-Incremental Learning With Strong Pre-Trained Models (CVPR 2022 · similarity 0.82)
- Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams. (ICLR 2023 · similarity 0.82)
- Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds (CVPR 2025 · similarity 0.81)
- Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement (ICLR 2025 · similarity 0.81)
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