7 papers · 1 filter
GrowTAS: Progressive Expansion from Small to Large Subnets for Efficient ViT Architecture Search
Hyunju Lee, Youngmin Oh, Jeimin Jeon +2
Transformer architecture search (TAS) aims to automatically discover efficient vision transformers (ViTs), reducing the need for manual design. Existing TAS methods typically train…
AccuQuant: Simulating Multiple Denoising Steps for Quantizing Diffusion Models
Seunghoon Lee, Jeongwoo Choi, Byunggwan Son +3
We present in this paper a novel post-training quantization (PTQ) method, dubbed AccuQuant, for diffusion models. We show analytically and empirically that quantization errors for…
Scheduling Weight Transitions for Quantization-Aware Training
Junghyup Lee, Jeimin Jeon, Dohyung Kim +1
Quantization-aware training (QAT) simulates a quantization process during training to lower bit-precision of weights/activations. It learns quantized weights indirectly by updating…
3DPillars: Pillar-based two-stage 3D object detection
Jongyoun Noh, Junghyup Lee, Hyekang Park +1
PointPillars is the fastest 3D object detector that exploits pseudo image representations to encode features for 3D objects in a scene. Albeit efficient, PointPillars is typically…
ELITE: Enhanced Language-Image Toxicity Evaluation for Safety
Wonjun Lee, Doehyeon Lee, Eugene Choi +5
Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for VLMs primarily rely on automated evaluation…
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search
Jeimin Jeon, Youngmin Oh, Junghyup Lee +4
N-shot neural architecture search (NAS) exploits a supernet containing all candidate subnets for a given search space. The subnets are typically trained with a static training stra…