Today I reviewed the results of the earlier experiments I ran with my mentor and other students in the lab. The most interesting result (which we most likely will have to repeat to ensure accuracy) was that the intra dataset OOD performance for the full rehearsal model was actually higher than that of the offline model.
The y-axis represents the omega value for intra datset OOD with mahalanobis. The x-axis represents the number of classes learned.
Today was also the RIT Undergraduate Research Symposium which was very fun to attend. Along with a few other interns, I listened to three presentations which talked about political biases affecting article credibility, fingerprinting as a means of cybersecurity defense, and laughter detection and classification using deep learning respectively. Each talk was interesting in its own way, and I enjoyed learning about the other research being performed in a similar field to the one I am working in.
Tomorrow I hope to run more experiments with an SLDA model to see if I achieve similar interesting results.
The y-axis represents the omega value for intra datset OOD with mahalanobis. The x-axis represents the number of classes learned.
Today was also the RIT Undergraduate Research Symposium which was very fun to attend. Along with a few other interns, I listened to three presentations which talked about political biases affecting article credibility, fingerprinting as a means of cybersecurity defense, and laughter detection and classification using deep learning respectively. Each talk was interesting in its own way, and I enjoyed learning about the other research being performed in a similar field to the one I am working in.
Tomorrow I hope to run more experiments with an SLDA model to see if I achieve similar interesting results.
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