Releases: lbechberger/LearningPsychologicalSpaces
Releases · lbechberger/LearningPsychologicalSpaces
LearningPsychologicalSpaces: Learning a Mapping From Images Into MDS-Based Similarity Spaces
- added statistical analysis of the raw data set (conceptual vs. visual dissimilarity ratings, pre-attentive vs. attentive feature ratings, correlation of feature ratings across different features)
Machine Learning Study with CNNs on Shapes Data
- Added experiments with autoencoder structure
Machine Learning Study with CNNs on Shapes Data
- added code for training a CNN from scratch on sketches and line drawings
- several experiments for transfer learning, multi-task learning, and generalization to other target spaces
Study on Multidimensional Scaling and Neural Networks on the NOUN Dataset
- major refactoring of the code base
- added functionality with respect to Shapes study
- re-ran experiments with updated code
Study on Multidimensional Scaling and Neural Networks on the NOUN Dataset
- Multiple minor updates on scripts (also with respect to the forthcoming Shapes study)
- re-ran the whole pipeline (updated results)
Study on Multidimensional Scaling and Neural Networks on the NOUN Dataset
A thorough update of our study on the NOUN dataset:
- analyzing different MDS algorithms (metric vs. nonmetric)
- two pixel-based baselines (downscaled images and ANN activations)
- Scree plot and correlation analysis
- 3 machine learning experiments varying feature space, MDS algorithm, and dimensionality of target space
Study on Multidimensional Scaling and Neural Networks on the NOUN Dataset
A thorough update of our study on the NOUN dataset:
- analyzing different MDS algorithms (metric vs. nonmetric)
- two pixel-based baselines (downscaled images and ANN activations)
- Scree plot and correlation analysis
- 3 machine learning experiments varying feature space, MDS algorithm, and dimensionality of target space
LearningPsychologicalSpaces v0.1
First pre-release of our code as used for the paper "Mapping Images to Psychological Similarity Spaces Using Neural Networks" (submitted to AIC 2018).