Learning Transferrable Representations for Unsupervised Domain Adaptation
Authors: Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the proposed method significantly outperforms state-of-the-art algorithms in both object recognition and digit classification experiments by a large margin. |
| Researcher Affiliation | Collaboration | Ozan Sener1, Hyun Oh Song1, Ashutosh Saxena2, Silvio Savarese1 Stanford University1 Brain of Things2 {ozan,hsong,asaxena,ssilvio}@cs.stanford.edu |
| Pseudocode | Yes | Algorithm 1 Transduction with Domain Shift |
| Open Source Code | Yes | Learned models and the source code can be reached from the project webpage http://cvgl.stanford.edu/transductive_adaptation. |
| Open Datasets | Yes | We use MNIST [19], Street View House Number [21] and the artificially generated version of MNIST -MNIST-M[11] to experiment our algorithm on the digit classification task. (...) In addition, we use the Office [25] dataset to evaluate our algorithm on the object recognition task. |
| Dataset Splits | No | The paper evaluates in a transductive setup, feeding 'training images' for source and target domains, but does not specify train/validation/test splits with percentages, counts, or predefined split citations for reproducibility of data partitioning for the model training itself. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions using AlexNet and LeNet architectures and AdaGrad, but does not specify version numbers for any software dependencies or frameworks. |
| Experiment Setup | Yes | We set the feature dimension as 128. We use stochastic gradient descent to learn the feature function with Ada Grad[8]. We initialize convolutional weights with truncated normals having std-dev 0.1, biases with constant value 0.1, and use a learning rate of 2.5e-4 with batch size 512. We start the rejection penalty with γ = 0.1 and linearly increase with each epoch as γ = #epoch/M + 0.1. In our experiments, we use M = 20, λ = 0.001 and α = 1. |