BRIDLE: Generalized Self-supervised Learning with Quantization

arXiv:2502.02118 · cs.LG, cs.CV · Submitted 2025-02-04 · Read on arXiv

cs.LG, cs.CV

Submitted: 2025-02-04

Updated: 2026-08-26

Code: https://github.com/HoangNguyenM/bridle

Terminology

• Quantization — 56× in this paper · explained in 12 episodes
• Residual Quantization — 25× in this paper
• Self-Supervised Learning — 16× in this paper · explained in 8 episodes
• Representation Learning — 15× in this paper · explained in 2 episodes
• Fine-Tuning — 13× in this paper · explained in 5 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Embeddings — 10× in this paper · explained in 2 episodes
• Vector Quantization — 8× in this paper
• Latent Space — 7× in this paper · explained in 7 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Linear Probing — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Contrastive Learning — 5× in this paper · explained in 5 episodes
• Loss Function — 4× in this paper · explained in 2 episodes
• Vector Quantization (VQ) — 4× in this paper · explained in 2 episodes
• Bidirectional Residual Quantization Interleaved Discrete Learning Encoder — 3× in this paper
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Tokenization — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Code Usage Rate — 2× in this paper
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Effective Code Usage — 2× in this paper
• Exponential Moving Average — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning (SSL) — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Usage Entropy — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Jukebox: A Generative Model for Music
• The Llama 3 Herd of Models
• The Kinetics Human Action Video Dataset
• BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
• Representation Learning with Contrastive Predictive Coding

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