9 papers
Say My Name: a Model's Bias Discovery Framework
Massimiliano Ciranni, Luca Molinaro, Carlo Alberto Barbano +4
In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases t…
Efficient Learned Image Compression Through Knowledge Distillation
Fabien Allemand, Attilio Fiandrotti, Sumanta Chaudhuri +1
Learned image compression sits at the intersection of machine learning and image processing. With advances in deep learning, neural network-based compression methods have emerged.…
Neural Velocity for hyperparameter tuning
Gianluca Dalmasso, Andrea Bragagnolo, Enzo Tartaglione +2
Hyperparameter tuning, such as learning rate decay and defining a stopping criterion, often relies on monitoring the validation loss. This paper presents NeVe, a dynamic training a…
Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
Cristiano PatrÃcio, Carlo Alberto Barbano, Attilio Fiandrotti +4
Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). I…
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
Gabriele Spadaro, Alberto Presta, Jhony H. Giraldo +5
Efficient compression of low-bit-rate point clouds is critical for bandwidth-constrained applications. However, existing techniques mainly focus on high-fidelity reconstruction, re…
Lightweight Embedded FPGA Deployment of Learned Image Compression with Knowledge Distillation and Hybrid Quantization
Alaa Mazouz, Sumanta Chaudhuri, Marco Cagnanzzo +3
Learnable Image Compression (LIC) has shown the potential to outperform standardized video codecs in RD efficiency, prompting the research for hardware-friendly implementations. Mo…