Weihang Zhang, Jia Guo, Shuai Lu, Lize Jia, Huiqi Li
We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| guojiajeremy/ReContrast | canonical | 1 of 1 |
| copy not recorded | — | 1 of 1 |
| guojiajeremy/recontrast | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| count_parameters | Ran | this paper's copy was not recorded; identical code first harvested from crisbodnar/ndp pointer only · get_code("f6b944f50d3f15ae") |
| get_logger | Ran | guojiajeremy/ReContrast/recontrast_mvtec.py pointer only (licence: NONE) · get_code("a1db6c55cfde6e19") |
| ReContrast | Not yet run | guojiajeremy/recontrast/models/recontrast.py pointer only (licence: NONE) · get_code("c145a0c0950aed81") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e.g. ImageNet. However, the features extracted from the encoders that are borrowed from natural image domains coincide little with the features required in the target UAD domain, such as industrial inspection and medical imaging. In this paper, we propose a novel epistemic UAD method, namely ReContrast, which optimizes the entire network to reduce biases towards the pre-trained image domain and orients the network in the target domain. We start with a feature reconstruction approach that detects anomalies from errors. Essentially, the elements of contrastive learning are elegantly embedded in feature reconstruction to prevent the network from training instability, pattern collapse, and identical shortcut, while simultaneously optimizing both the encoder and decoder on the target domain. To demonstrate our transfer ability on various image domains, we conduct extensive experiments across two popular industrial defect detection benchmarks and three medical image UAD tasks, which shows our superiority over current state-of-the-art methods. Code is available at: https://github.com/guojiajeremy/ReContrast *Corresponding Author 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.02602")
get_code_for_paper("2306.02602")
have("2306.02602")
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