Adaptive Cross-Modal Few-shot Learning
Authors: Chen Xing, Negar Rostamzadeh, Boris Oreshkin, Pedro O. O. Pinheiro
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested. |
| Researcher Affiliation | Collaboration | Chen Xing College of Computer Science, Nankai University, Tianjin, China Element AI, Montreal, Canada Negar Rostamzadeh Element AI, Montreal, Canada Boris N. Oreshkin Element AI, Montreal, Canada Pedro O. Pinheiro Element AI, Montreal, Canada |
| Pseudocode | Yes | Algorithm 1, on supplementary material, shows the pseudocode for calculating the episode loss. |
| Open Source Code | Yes | Source code is released at https://github.com/Element AI/am3. |
| Open Datasets | Yes | We conduct main experiments with two widely used few-shot learning datasets: mini Image Net [53] and tiered Image Net [39]. We also experiment on CUB-200 [55], a widely used zero-shot learning dataset. |
| Dataset Splits | Yes | Few-shot learning models are trained on a labeled dataset Dtrain and tested on Dtest. The class sets are disjoint between Dtrain and Dtest. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions using GloVe for word embeddings but does not specify software dependencies with version numbers (e.g., Python, PyTorch versions). |
| Experiment Setup | No | The paper states 'For details on network architectures, training and evaluation procedures, see Apprendix D.', but these details, including hyperparameters and training configurations, are not present in the main body of the paper provided. |