We lifted 7 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| n3il666/meta-dmoe | canonical | 4 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| get_model_accuracy | Ran | n3il666/meta-dmoe/src/camelyon/camelyon_experts.py code served (permissive licence) · get_code("f6e35c72666b64f6") |
| get_selector_accuracy | Ran | n3il666/meta-dmoe/src/fmow/fmow_aggregator.py code served (permissive licence) · get_code("e2705ed6b48d20e5") |
| initialize_image_base_transform | Ran | n3il666/meta-dmoe/src/camelyon/camelyon_utils.py code served (permissive licence) · get_code("48d7a611a78faa01") |
| initialize_image_base_transform | Ran | n3il666/meta-dmoe/src/fmow/fmow_utils.py code served (permissive licence) · get_code("69e94cb234255b0c") |
| get_expert_split | Not yet run | n3il666/meta-dmoe/src/camelyon/camelyon_experts.py code served (permissive licence) · get_code("a952a1fa7c7d8616") |
| get_model_accuracy | Not yet run | n3il666/meta-dmoe/src/fmow/fmow_experts.py code served (permissive licence) · get_code("f2746799d8f51cf0") |
| get_selector_accuracy | Not yet run | n3il666/meta-dmoe/src/camelyon/camelyon_aggregator.py code served (permissive licence) · get_code("c71f59f1661f12e4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we tackle the problem of domain shift. Most existing methods perform training on multiple source domains using a single model, and the same trained model is used on all unseen target domains. Such solutions are sub-optimal as each target domain exhibits its own specialty, which is not adapted. Furthermore, expecting single-model training to learn extensive knowledge from multiple source domains is counterintuitive. The model is more biased toward learning only domain-invariant features and may result in negative knowledge transfer. In this work, we propose a novel framework for unsupervised test-time adaptation, which is formulated as a knowledge distillation process to address domain shift. Specifically, we incorporate Mixture-of-Experts (MoE) as teachers, where each expert is separately trained on different source domains to maximize their specialty. Given a test-time target domain, a small set of unlabeled data is sampled to query the knowledge from MoE. As the source domains are correlated to the target domains, a transformer-based aggregator then combines the domain knowledge by examining the interconnection among them. The output is treated as a supervision signal to adapt a student prediction network toward the target domain. We further employ meta-learning to enforce the aggregator to distill positive knowledge and the student network to achieve fast adaptation. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art and validates the effectiveness of each proposed component. Our code is available at https://github.com/n3il666/Meta-DMoE.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.03885")
get_code_for_paper("2210.03885")
have("2210.03885")
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