Learning to Adapt to Evolving Domains
Authors: Hong Liu, Mingsheng Long, Jianmin Wang, Yu Wang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments validate the effectiveness our method on evolving domain adaptation benchmarks. |
| Researcher Affiliation | Academia | School of Software, KLiss, BNRist, Tsinghua University Department of Electronic Engineering, Tsinghua University |
| Pseudocode | Yes | Algorithm 1 Meta-Training of Evolution Adaptive Meta-Learning (EAML) |
| Open Source Code | Yes | 2Codes are available at https://github.com/Liuhong99/EAML. |
| Open Datasets | Yes | Rotated MNIST: This dataset consists of MNIST digits of various rotations. Evolving Vehicles: This dataset contains sedans and trucks from the 1970s to 2010s (See Figure 1). Caltran: This is a real-world dataset of images captured by a camera at an intersection over time. |
| Dataset Splits | Yes | In the meta-training phase we have access to adequate labeled examples from the source domain, and part of the target unlabeled data from a target domain evolving over time in the meta-training phase, (2) new target data of the meta-testing phase arrive sequentially online from the same evolving target distribution and cannot be stored... We randomly sample a trajectory T = {Xt1, Xt2 Xtn}, and a target trajectory from the query set as Tqry = {X t1, X t2 X tn}. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper states, 'We implement our method on Py Torch,' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use SGD with 0.9 momentum and 5 10 4 weight decay. The learning rates of the inner loop and the outer loop are set to 0.01 and 0.001 respectively. For rotated MNIST, we use Le Net [16] as the backbone. The meta-representation fθ includes two convolutional layers. The adapter is a two-layer fully-connected network with Re LU activations. For Evolving Vehicles and Caltran, we use a six-layer convolutional network as the backbone. The meta-representation fθ includes four convolutional layers... The adapter is a two-layer convolutional network with Re LU activations. |