Update README.md

README.md

@@ -7,7 +7,41 @@ language:
 pipeline_tag: audio-classification
 ---
 
+# SNN Voice
 
-# SNN Voice Trained Models
+SNN Voice is the repository for my paper: "Retraining SNN Conversions: CNN to SNN for Audio Classification Tasks".
+The models are deposited here for archival.
 
-**Important** Piczak's SNN Model is excluded due to extremely large size
+See our code repository for implementation @ GitHub: https://github.com/Eve-ning/snn_voice
+
+## Abstract
+
+Efficient yet powerful models are in high demand for its portability and affordability.
+Amongst other methods such as model-pruning, is limiting neural network operations to
+sparse event-driven spikes: Spiking Neural Networks (SNNs) aims to unravel a new di-
+rection in machine learning research. A significant amount of SNN literature straddles
+upon mature works of artificial neural networks (ANNs) by migrating its architecture
+and parameters into SNNs, optimizing the migration to retain as much performance as
+possible. We spearhead a novel approach: the architecture is migrated and retrained
+from scratch. We hypothesize that this new direction will unravel concepts that cur-
+rently bottlenecks improvements in the field of SNN conversions. Furthermore, alike
+Transfer Learning, inspire future efforts of fine-tuning a well converted model through
+training.
+
+This paper presents our analysis of training converted Convolutional Neural Networks
+(CNNs) to SNNs on audio classification models. Results show that (1) SNN conver-
+sions consistently underperforms CNNs marginally during training, however we also
+show that model complexity has a possible association with this margin. (2) SNN con-
+verts doesn’t necessarily approach the performance of its CNN counterparts asymptot-
+ically by increasing the number of time-steps. (3) SNN training from scratch is costly
+and impractical with current hardware and dedicated SNN optimization techniques are
+necessary. (4) Enabling the SNN membrane decay rate to be learned doesn’t signifi-
+cantly affect performance. This paper provides valuable insights into the perspective of
+retraining converted SNNs for audio classification, and serves as a reference for future
+studies and hardware implementation benchmarks.
+
+## SNN Voice Trained Models
+
+**Important**
+
+We only upload Piczak's SNN Model for 15 time-steps with Learnable Beta = False due to it's extremely large size (300MB)