# SimCDL: A Simple Framework for Contrastive Dictionary Learning
## 6 Conclusions  
This paper presents a novel framework for initializing a dictionary using the SimCLR technique. The framework is suitable for general dictionary learning problems, classification problems that involve multiple dictionaries, and anomaly detection tasks. This approach makes the atoms in the dictionary more incoherent, resulting in a better representation of the input data and improved representation errors. The results obtained from several datasets demonstrate the efficacy of this framework for applications such as classification and outlier detection. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & **AK-SVD** & **AK-SVD\({}^{\star}\)** & **\# Samples** & **\# Features** & **\# Anomaly** & **Category** \\ \hline annthyroid & 0.782634 & **0.789434** & 7200 & 6 & 534 & Healthcare \\ breastw & **0.813422\({}^{2}\)** & 0.810371 & 683 & 9 & 239 & Healthcare \\ cardio & **0.976233\({}^{2}\)** & 0.975875\({}^{3}\) & 1831 & 21 & 176 & Healthcare \\ cover & 0.942390 & **0.945926** & 286,048 & 10 & 2747 & Botany \\ fault & **0.994302\({}^{2}\)** & 0.993869\({}^{3}\) & 1941 & 27 & 673 & Physical \\ glass & 0.854623\({}^{2}\) & **0.873868\({}^{1}\)** & 214 & 7 & 9 & Forensic \\ Hepatitis & 0.894448\({}^{3}\) & **0.901473\({}^{2}\)** & 80 & 19 & 13 & Healthcare \\ Ionosphere & 0.990133 & **0.990810** & 351 & 32 & 126 & Orcytognosy \\ landsat & **0.999071\({}^{1}\)** & 0.999070\({}^{3}\) & 6435 & 36 & 1333 & Astronautics \\ letter & **0.998711\({}^{1}\)** & 0.99860\({}^{2}\) & 1600 & 32 & 100 & Image \\ Lymphography & 0.954972\({}^{2}\) & **0.955347\({}^{1}\)** & 148 & 18 & 6 & Healthcare \\ magic.gamma & 0.899432 & **0.901307** & 19,020 & 10 & 6688 & Physical \\ mammography & 0.817071 & **0.831252** & 11,183 & 6 & 260 & Healthcare \\ musk & 0.998768\({}^{3}\) & **0.999148\({}^{2}\)** & 3062 & 166 & 97 & Chemistry \\ pendigits & 0.980712 & **0.983217** & 6870 & 16 & 156 & Image \\ Pima & 0.809388 & **0.814498\({}^{3}\)** & 768 & 8 & 268 & Healthcare \\ satellite & 0.999136\({}^{3}\) & **0.999298\({}^{2}\)** & 6435 & 36 & 2036 & Astronautics \\ satimage & 0.999900\({}^{2}\) & **0.999934\({}^{1}\)** & 5803 & 36 & 71 & Astronautics \\ SpamBase & **0.908187** & 0.907505 & 4207 & 57 & 1679 & Document \\ Stamps & 0.856465 & **0.859910** & 340 & 9 & 31 & Document \\ thyroid & 0.820620 & **0.830234** & 3772 & 6 & 93 & Healthcare \\ vertebral & 0.858781 & **0.809556\({}^{1}\)** & 240 & 6 & 30 & Biology \\ vowels & 0.932859 & **0.932561** & 1456 & 12 & 50 & Linguistics \\ Waveform & 0.992237\({}^{3}\) & **0.992642\({}^{2}\)** & 3443 & 21 & 100 & Physics \\ WBC & 0.823793 & **0.825724\({}^{3}\)** & 223 & 9 & 10 & Healthcare \\ WDBC & **0.998545\({}^{2}\)** & 0.998288 & 367 & 30 & 10 & Healthcare \\ Wilt & **0.726567\({}^{3}\)** & 0.726060 & 4819 & 5 & 257 & Botany \\ wine & **0.956704\({}^{3}\)** & 0.952583 & 129 & 13 & 10 & Chemistry \\ WPBC & 0.993470 & **0.994286\({}^{3}\)** & 198 & 33 & 47 & Healthcare \\ yeast & 0.880533\({}^{3}\) & **0.891971\({}^{2}\)** & 1484 & 8 & 507 & Biology \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ADBench results. The best result for each dataset is in bold. Future work will concentrate on enhancing the SimCDL framework, with a particular focus on the development of incoherent atoms within the DL problems in a supervised manner. Conceptualization, D.C.I.-A. and B.D. ; methodology, D.C.I.-A. and B.D. ; software, D.C.I.-A. ; validation, D.C.I.-A. and B.D. ; formal analysis, D.C.I.-A. and B.D. ; investigation, D.C.I.-A. ; data curation, D.C.I.-A. ; writing--original draft preparation, D.C.I.-A. ; writing--review and editing, B.D. and D.C.I.-A. ; visualization, D.C.I.-A. ; supervision, B.D. All authors have read and agreed to the published version of the manuscript. This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS--UEFISCDI, project number PN-III-P4-PCE-2021-0154, within PNCDI III. Not applicable.