Agentic Design

Patterns
🔍

Unsupervised Learning for Agents(ULA)

Discovering hidden patterns and structures in unlabeled data to enhance agent understanding

Complexity: highLearning and Adaptation

🎯 30-Second Overview

Pattern: Adapt models to new domains using unlabeled data through self-supervised learning objectives

Why: Leverages abundant unlabeled data to learn domain-specific patterns without expensive annotation requirements

Key Insight: Self-supervised pretext tasks create supervisory signals from data structure, enabling effective domain adaptation

⚡ Quick Implementation

1Collect:Gather unlabeled target domain data
2Preprocess:Clean and structure data without labels
3Self-supervise:Apply self-supervised learning objectives
4Adapt:Fine-tune on domain-specific patterns
5Evaluate:Assess adaptation using proxy tasks or downstream performance
Example: pretrained_model + unlabeled_domain_data → self_supervised_objectives → adapted_model

📋 Do's & Don'ts

✅Use diverse self-supervised objectives (masking, contrastive, generative)
✅Implement domain-specific data augmentation strategies
✅Monitor representation quality through probing tasks
✅Apply gradual unfreezing and layer-wise adaptation
✅Use contrastive learning for robust feature extraction
✅Validate on downstream tasks to measure adaptation success
❌Ignore data quality and distribution characteristics
❌Use inappropriate self-supervised objectives for domain
❌Overtrain without proper regularization
❌Skip evaluation of learned representations
❌Apply without understanding domain-specific patterns

🚦 When to Use

Use When

  • • Large amounts of unlabeled domain data are available
  • • Labeled data is expensive or impossible to obtain
  • • Need to adapt to new domains with different distributions
  • • Self-supervised signals can be extracted from data structure
  • • Domain has rich inherent patterns and regularities

Avoid When

  • • High-quality labeled data is readily available
  • • Domain lacks clear self-supervised signals
  • • Computational resources are severely limited
  • • Immediate deployment without adaptation time
  • • Simple transfer learning is sufficient

📊 Key Metrics

Representation Quality
Performance on probing tasks and linear evaluation
Domain Adaptation Score
Improvement over pre-trained baseline on target domain
Self-Supervised Loss
Convergence and stability of unsupervised objectives
Downstream Performance
Task performance after adaptation
Transfer Efficiency
Learning speed on target domain tasks
Robustness
Performance under domain shift and noise

💡 Top Use Cases

Domain Adaptation: Adapt models to new industries, languages, or specialized domains without labels
Cross-Modal Learning: Learn shared representations between text, images, and other modalities
Temporal Adaptation: Adapt models to evolving data distributions over time
Low-Resource Languages: Adapt language models to languages with limited labeled data
Scientific Data Analysis: Learn patterns in specialized scientific datasets without annotations
Privacy-Preserving Adaptation: Adapt models without accessing sensitive labeled information

References & Further Reading

Deepen your understanding with these curated resources

Foundational Self-Supervised Learning

Self-Supervised Learning: Generative or Contrastive (Liu et al., 2021)

A Simple Framework for Contrastive Learning of Visual Representations (Chen et al., 2020)

Momentum Contrast for Unsupervised Visual Representation Learning (He et al., 2020)

Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Grill et al., 2020)

Language Model Self-Supervision

BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2018)

RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)

DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020)

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (Clark et al., 2020)

Domain Adaptation Techniques

Domain-Adversarial Training of Neural Networks (Ganin et al., 2016)

Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)

Deep CORAL: Correlation Alignment for Deep Domain Adaptation (Sun & Saenko, 2016)

Adversarial Discriminative Domain Adaptation (Tzeng et al., 2017)

Contrastive Learning Methods

SimCLR: A Simple Framework for Contrastive Learning (Chen et al., 2020)

SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Caron et al., 2020)

SimCSE: Simple Contrastive Learning of Sentence Embeddings (Gao et al., 2021)

SupCon: Supervised Contrastive Learning (Khosla et al., 2020)

Masked Language Modeling

BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2018)

SpanBERT: Improving Pre-training by Representing and Predicting Spans (Joshi et al., 2020)

ERNIE: Enhanced Representation through Knowledge Integration (Zhang et al., 2019)

ALBERT: A Lite BERT for Self-supervised Learning (Lan et al., 2019)

Recent Advances (2023-2024)

MAE: Masked Autoencoders Are Scalable Vision Learners (He et al., 2022)

SimMIM: A Simple Framework for Masked Image Modeling (Xie et al., 2022)

BEiT: BERT Pre-Training of Image Transformers (Bao et al., 2021)

Data2vec: A General Framework for Self-supervised Learning (Baevski et al., 2022)

Multimodal Self-Supervision

CLIP: Learning Transferable Visual Representations from Natural Language Supervision (Radford et al., 2021)

ALIGN: Scaling Up Visual and Vision-Language Representation Learning (Jia et al., 2021)

Florence: A New Foundation Model for Computer Vision (Yuan et al., 2021)

DALLE-2: Hierarchical Text-Conditional Image Generation (Ramesh et al., 2022)

Cross-Domain Adaptation

Domain Adaptation for Neural Networks: A Review (Farahani et al., 2021)

Unsupervised Cross-domain Representation Learning (Hoffman et al., 2018)

Deep Transfer Learning: A New Deep Learning Research Direction (Tan et al., 2018)

A Survey on Deep Transfer Learning and Domain Adaptation (Wilson & Cook, 2020)

Implementation Frameworks

PyTorch Self-Supervised Learning Library (PyTorch-SSL)

SimCLR Implementation in TensorFlow

Hugging Face Transformers - Self-Supervised Models

Self-Supervised Learning Zoo (SSL-Zoo)

Evaluation & Benchmarks

ImageNet Linear Evaluation Protocol

SentEval: Evaluation Toolkit for Sentence Embeddings

GLUE: General Language Understanding Evaluation

Domain Adaptation Benchmarks (Office-31, VisDA)

Tools & Libraries

VISSL: Computer Vision Self-Supervised Learning

Lightly: Self-Supervised Learning Framework

Solo-learn: Self-Supervised Learning Library

TorchSSL: PyTorch SSL Implementation

Production Applications

Facebook AI: Self-Supervised Learning at Scale

Google Research: SimCLR and Beyond

OpenAI CLIP: Production Multimodal Models

DeepMind: Self-Supervised Learning Research

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