# SimCDL: A Simple Framework for Contrastive Dictionary Learning
## 5 Experiments
### Classification  
This section contains experiments carried out with the SimCDL framework. For our tests, we reproduce the results of the algorithms presented in the previous section, named Incoherent Dictionary Learning (IDL), Incoherent Dictionary Learning via Distance Barrier (IDB), and Incoherent Dictionary Learning via Triplet Distance Barrier (ITDB). We used several popular datasets that are widely used in computer vision and machine learning. Some of them were explicitly adapted for DL problems. The procedure for obtaining these datasets is available in [11]. These datasets were extensively used for research in face recognition, scene classification, object recognition, and human action recognition. In the following, we provide some general details for each dataset. The _YaleB Face Dataset_[19] is a well-known collection of face images for studying problems in the field of computer vision, such as face recognition and facial expression categorization. It consists of 2414 grayscale images of 38 different persons. This dataset is particularly challenging due to the variations in lighting and facial expressions. The _AR Face Dataset_[20] comprises over 4000 color images corresponding to 126 people's faces (70 men and 56 women). The images feature different facial expressions, illumination conditions, and occlusions. The pictures were taken under varying conditions, making the dataset suitable for evaluating robustness in facial recognition tasks. The _CMU Pose, Illumination, and Expression (PIE) dataset_[21] contains 41,368 images of 68 people, each captured under 13 different poses, 43 different illumination conditions, with 4 different expressions. The CMU PIE dataset is extensively used to develop and test algorithms for face recognition across pose and illumination variations and is one of the largest databases of its kind. The _Scene 15 dataset_[21] is a collection of scene images categorized into 15 categories, including office, kitchen, street, and living room, totaling over 4000 images. This dataset has been used to evaluate algorithms for scene recognition. It is notable for its scale, resolution, and subject matter variety, presenting a realistic benchmark for scene classification tasks. _Caltech101_[22] consists of a database of 9144 images, subdivided into 101 object categories plus a background clutter category. With an average of 40 to 800 images per category, the dataset includes various objects taken from different angles and backgrounds. It has been a standard for assessing the performance of algorithms in object recognition and category classification. The _UCF50 dataset_[23] is an action recognition dataset consisting of 50 action categories, each from videos in the wild. It contains realistic action videos collected from YouTube, totaling over 6680 clips. The _UCF50_ dataset is designed to evaluate human action recognition algorithms in varied scenarios, capturing the complexities of appearance, motion, interaction, and context. Table 1 presents a general description of these datasets. In our experimental setup, we utilized the augmentation techniques described in Section 3: cutout, rotation, Gaussian noise, and Gaussian blur. A systematic grid search was performed for various parameters: rotation angles were set at \(\alpha\in\{0^{\circ},1^{\circ},2^{\circ},3^{\circ},4^{\circ}\}\); cutout percentages were \(\{0\%,20\%,40\%,60\%,80\%\}\); standard deviations for noise were \(\{0,0.2,0.4\}\) and for blur were \(\{0,0.25,0.5\}\). The batch sizes tested included \(\{16,32,64,128\}\), and the dictionary was updated across \(\{5,10,20\}\) iterations. Optimal parameters identified for most datasets were a rotation angle of \(2^{\circ}\), a noise standard deviation of 0.2, a blur standard deviation of 0.5, and a cutout percentage of 20%. The temperature parameter, \(\tau\), was set to 0.5. All subsequent experiments were conducted with batch sizes of 16 pairs of samples, resulting in 32 augmented samples in total.