The Adversarial Robustness Toolbox (ART) = framework that uses generative adversarial neural networks (GANs) to protect deep learning models from security attacks
Thread⬇️
GANs = the most popular form of generative models.
GAN-based attacks:
+White Box Attacks: The adversary has access to the training environment, knowledge of the training algorithm
+Black Box Attacks: The adversary has no additional knowledge
2/⬇️
The goal of ART = to provide a framework to evaluate the robustness of a neural network.
The current version of ART focuses on four types of adversarial attacks:
+evasion
+inference
+extraction
+poisoning
3/⬇️
ART is a generic Python library. It provides native integration with several deep learning frameworks such as @TensorFlow, @PyTorch, #Keras, @ApacheMXNet
If you'd like to find a concentrated coverage of ART, click the link below. You'll move to TheSequence Edge#7, our educational newsletter. thesequence.substack.com/p/edge7 5/5
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SingLoRA is a new simple version of LoRA (Low Rank Adaptation) by Technion that uses only one small matrix instead of usual two.
It multiplies it by its own transpose (like A × Aᵀ).
What does it buy you?
- No scale mismatch between different matrices
- Uses ~half the parameters of LoRA
- Stability and better learning
Here's how it works:
1. Workflow of SingLoRA:
• The original weights of the model (W₀) are frozen.
• The system adds a small adapter - a learnable piece that updates the model for your specific task.
In SigLoRA, it's A × Aᵀ, where:
- A is a small trainable matrix with n × r size, where r ≪ n
- Aᵀ is its transpose
• The original model and the adapter are combined like this:
2. SingLoRA is extended for all layer shapes, whether they are:
- Square (same input/output size), like many attention layers
- Rectangular (input ≠ output size), like MLP layers
- Non-square (here “truncated” version of A is used so the shapes line up correctly).
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