Meeting 03/11
Done
- Virtual machine setup & running experiments
- speed is x3 slower than RTX 3090
Started working on
- develop experiments with new dataset (Caltech101)
- assess experiments
- test transfer learning capabilities of CR-VAE
MS Students Updates
- Melanie
- Object Detection using Resnet
- Julian
- basic tutorials on ROS
- Teleoperation of Turtlebot using PS5 controller
- UR3 on ROS using MoveIt
Next on
- description of caltech
- talk with Konrad about the cluster
- Update paper with correct results and send it to CVPR
- Hyperparameter grid search experiments
- literature review on representation learning
- experiments with artificial datasets
- develop methods
- contrastive learning for spiking neural networks.
- mode-seeking kl divergence
Meeting 11/11
Progress
- Updated draft of the CR-VAE paper.
- Missed author registration deadline for CVPR >_<*
- Run experiments with simple AE / CR-AE
- Test if InfoMax objective actually works -> it doesn’t.
- New implementation of the loss function
- Tried input normalization and MSE for reconstruction error
- Assess experiments
- Hyperparameter grid search experiments for CR-VAE
- Literature review on representation learning (review paper)
- Going through ROS 2 documentation
- Get acquanted with UR3
MS Students Updates
- Melanie
- Object Detection using Resnet
- Next on: Image Segmentation
- Julian
- Teleoperation of Turtlebot using PS5 controller
- Next on: UR3 on Gazebo using MoveIt
Other activities
- Discussion with Sahar and Vedant about how Sahar could frame her Reinforcement learning research problem.
Next on
- assess experiments from grid search with the new loss function
- hopefully there will be some distinct difference of the 3 methods
- further assess the value of MI as an auxilary task for unsupervised representation learnning
- show that MI in InfoMax actually introduce noise
- Denoising AE/CR-AE/VAE/CR-VAE with augmented images.
- develop experiments with new dataset (Caltech101)
- description of caltech
- test transfer learning capabilities of CR-VAE
- literature review on representation learning
- experiments with artificial datasets
- develop methods
- contrastive learning for spiking neural networks.
- mode-seeking kl divergence
Journal ideas
- Find the best CL method for CR-VAE
- Transfer learning
Meeting 17/11
Progress
- gpu grid setbacks
- caltech101 dataset
- ROS2 refresh
- new results
MS Students Updates
- Melanie
- Image Segmentation on Steel Defect dataset
- Next on: Contrastive Learning
- Julian
- Sick
- Next on: UR3 on Gazebo using MoveIt
Other activities
- Study abroad fair speech
Next on
- study the big performance gap in KLD between CR-VAE and VAE
- further assess the value of MI as an auxilary task for unsupervised representation learnning
- show that MI in InfoMax actually introduce noise
- Denoising AE/CR-AE/VAE/CR-VAE with augmented images.
- develop experiments with new dataset (Caltech101)
- description of caltech
- test transfer learning capabilities of CR-VAE
- literature review on representation learning
- experiments with artificial datasets
- develop methods
- contrastive learning for spiking neural networks.
- mode-seeking kl divergence
Journal ideas
- Find the best CL method for CR-VAE
- Transfer learning
- develop a contrastive regularization layer for NN
Meeting 23/11
Updates
- AAAI paper submission update
- received an email that the file was never uploaded even though I have a verification email. Still in the process of figuring out
- new results on smaller architecture -> more distinct results
MS Students Updates
- Melanie
- Image Segmentation on Steel Defect dataset
- Next on: Contrastive Learning
- Julian
- Dropped
- Subject was not aligned with his program
- working at the lab did not fit his schedule
Other activities
- christmas & hololens 2 unboxing videos
- storage place or display for PS5 controler, hololense, etc?
- plan to publish the AR project as internship position
- LinkedIn -> CPS page?
- MUL
- Emails
Next on
Meeting 30/11
Updates
- experiments with caltech 101
- too small dataset. Network needs pretraining
- too big bictures. problems with gpu memory when training and big storage space when saving models
- refactor code to better scale for more evaluation techniques
- reviewed XAI methods.
- Further literature review for representation learning
MS Student Updates
- Melanie
- Image Segmentation on Steel Defect dataset
- Next on: Deep Optical Flow
Other activities
- Hololens 2 review
- plan to publish the AR project as internship position
- LinkedIn -> CPS page?
- MUL
- Emails
- Share Christmas video with the public relations team of MUL
- linked in account -> page
Next on