Jose Dolz, Malik Boudiaf, Éts Montreal, Ziko Masud, Jérôme Rony, Pablo Piantanida, Ismail Ben, Ayed Montreal
We lifted 10 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| mboudiaf/TIM | canonical | 6 of 7 |
| sicara/easy-few-shot-learning | — | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| FewShotClassifier | Ran | sicara/easy-few-shot-learning/easyfsl/methods/tim.py code served (permissive licence) · get_code("637e871cf76bfc10") |
| TIM | Ran | sicara/easy-few-shot-learning/easyfsl/methods/tim.py code served (permissive licence) · get_code("c46ba9a1cacbe109") |
| compute_prototypes | Ran | sicara/easy-few-shot-learning/easyfsl/methods/tim.py code served (permissive licence) · get_code("31e41d7e676cb492") |
| conv_block | Ran | mboudiaf/TIM/src/models/Conv4.py code served (permissive licence) · get_code("02652b1ac1a54dda") |
| get_features | Ran | mboudiaf/TIM/src/utils.py code served (permissive licence) · get_code("12cbd4d101797d30") |
| get_metric | Ran | mboudiaf/TIM/src/models/ProtoNet.py code served (permissive licence) · get_code("235da783eeb0573f") |
| get_one_hot | Ran | mboudiaf/TIM/src/utils.py code served (permissive licence) · get_code("a03c85ae21c3b667") |
| with_augment | Ran | mboudiaf/TIM/src/datasets/transform.py code served (permissive licence) · get_code("97533e04cdf4da7e") |
| without_augment | Ran | mboudiaf/TIM/src/datasets/transform.py code served (permissive licence) · get_code("e1f7a44f9ac823d7") |
| get_logs_path | Not yet run | mboudiaf/TIM/src/utils.py code served (permissive licence) · get_code("e232226ad7c9556f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions for a given few-shot task, in conjunction with a supervision loss based on the support set. Furthermore, we propose a new alternating-direction solver for our mutual-information loss, which substantially speeds up transductiveinference convergence over gradient-based optimization, while yielding similar accuracy. TIM inference is modular: it can be used on top of any base-training feature extractor. Following standard transductive few-shot settings, our comprehensive experiments 2 demonstrate that TIM outperforms state-of-the-art methods significantly across various datasets and networks, while used on top of a fixed feature extractor trained with simple cross-entropy on the base classes, without resorting to complex meta-learning schemes. It consistently brings between 2% and 5% improvement in accuracy over the best performing method, not only on all the well-established few-shot benchmarks but also on more challenging scenarios, with domain shifts and larger numbers of classes.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2008.11297")
get_code_for_paper("2008.11297")
have("2008.11297")
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